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GeroScience Jun 2024A growing body of research suggested that there was a link between poor periodontal health and systemic diseases, particularly with the early development of cognitive... (Review)
Review
A growing body of research suggested that there was a link between poor periodontal health and systemic diseases, particularly with the early development of cognitive disorders, dementia, and depression. This is especially true in cases of changes in diet, malnutrition, loss of muscular endurance, and abnormal systemic inflammatory response. Our study aimed to determine the extent of these associations to better target the multi-level healthy aging challenge investigating the impact of periodontal disease on cognitive disorders (cognitive impairment and cognitive decline), dementia, and depression. We conducted a comprehensive literature search up to November 2023 using six different electronic databases. Two independent researchers assessed the eligibility of 7363 records against the inclusion criteria and found only 46 records that met the requirements. The study is registered on PROSPERO (CRD42023485688). We generated random effects pooled estimates and 95% confidence intervals (CI) to evaluate whether periodontal disease increased the risk of the investigated outcomes. The quality assessment revealed moderate quality of evidence and risk of bias. Periodontal disease was found to be associated with both cognitive disorders (relative risk (RR) 1.25, 95% CI 1.11-1.40, in the analysis of cross-sectional studies); cognitive impairment (RR 3.01, 95% CI 1.52-5.95 for longitudinal studies, cognitive decline); and dementia (RR 1.22, 95% CI 1.10-1.36). However, no significant increased risk of depression among subjects with periodontal disease was found (RR 1.07, 95% CI 0.95-1.21). Despite the association with two of the three explored outcomes, the available evidence on periodontal diseases and dementia, cognitive disorders, and depression is controversial due to several limitations. Therefore, further investigations involving validated and standardized tools are required.
PubMed: 38943006
DOI: 10.1007/s11357-024-01243-8 -
Regional Anesthesia and Pain Medicine Jun 2024Peer review represents a cornerstone of the scientific process, yet few studies have evaluated its association with scientific impact. The objective of this study is to...
How predictive is peer review for gauging impact? The association between reviewer rating scores, publication status, and article impact measured by citations in a pain subspecialty journal.
BACKGROUND
Peer review represents a cornerstone of the scientific process, yet few studies have evaluated its association with scientific impact. The objective of this study is to assess the association of peer review scores with measures of impact for manuscripts submitted and ultimately published.
METHODS
3173 manuscripts submitted to between August 2018 and October 2021 were analyzed, with those containing an abstract included. Articles were categorized by topic, type, acceptance status, author demographics and open-access status. Articles were scored based on means for the initial peer review where each reviewer's recommendation was assigned a number: 5 for 'accept', 3 for 'minor revision', 2 for 'major revision' and 0 for 'reject'. Articles were further classified by whether any reviewers recommended 'reject'. Rejected articles were analyzed to determine whether they were subsequently published in an indexed journal, and their citations were compared with those of accepted articles when the impact factor was 1.4 points lower than 's 5.1 impact factor. The main outcome measure was the number of Clarivate citations within 2 years from publication. Secondary outcome measures were Google Scholar citations within 2 years and Altmetric score.
RESULTS
422 articles met inclusion criteria for analysis. There was no significant correlation between the number of Clarivate 2-year review citations and reviewer rating score (r=0.038, p=0.47), Google Scholar citations (r=0.053, p=0.31) or Altmetric score (p=0.38). There was no significant difference in 2-year Clarivate citations between accepted (median (IQR) 5 (2-10)) and rejected manuscripts published in journals with impact factors 3.7 (median 5 (2-7); p=0.39). Altmetric score was significantly higher for -published papers compared with -rejected ones (median 10 (5-17) vs 1 (0-2); p<0.001).
CONCLUSIONS
Peer review rating scores were not associated with citations, though the impact of peer review on quality and association with other metrics remains unclear.
PubMed: 38942427
DOI: 10.1136/rapm-2024-105490 -
Behavioural Processes Jun 2024Pairing a palatable flavor (US) with an initially neutral flavor cue (CS) results in an acquired conditioned preference for the latter. Two main associations have been...
Pairing a palatable flavor (US) with an initially neutral flavor cue (CS) results in an acquired conditioned preference for the latter. Two main associations have been proposed to explain the acquisition of flavor preferences: Flavor-Flavor and Flavor-Nutrient learning. Although the hedonic reaction triggered by US consumption has also been suggested as a possible additional component underlying acquired flavor preference, this issue has received little attention. Here we explored whether the amount of training to the CS-US compound can favor the formation of a Flavor-Hedonic reaction association using rats as subjects and sucrose as the US. We expected that the more exposure to the CS-US compound, the stronger the S-R type association. Since S-R associations are not sensitive to devaluation procedures, we used a Sensory-Specific Satiety procedure to devalue the US after conditioning and then measured preferences for the CS. On Experiment 1 with a short restrictive training (classic procedure), preference for the CS was decreased after devaluation of the US compared to the control condition. On Experiment 2, with short unrestrictive training, preference for the CS was again weakened. Experiment 3 with a long unrestrictive training, rats expressed preference for the CS regardless of the devaluation procedure. These results suggest that, as with an instrumental paradigm, extensive training in flavor preference learning undermines the US devaluation effect.
PubMed: 38942399
DOI: 10.1016/j.beproc.2024.105074 -
Journal of Vascular Surgery Jun 2024Outcomes for weekend surgical interventions are associated with higher rates of mortality and complications compared to weekday interventions. While prior investigations...
OBJECTIVE
Outcomes for weekend surgical interventions are associated with higher rates of mortality and complications compared to weekday interventions. While prior investigations have reported the 'weekend effect' for carotid endarterectomy (CEA), this association remains unclear for Transcarotid Artery Revascularization (TCAR) and Transfemoral Carotid Artery Stenting (TFCAS). We investigated the weekend effect for all three carotid revascularization methods.
METHODS
We queried the Vascular Quality Initiative (VQI) for patients undergoing CEA, TCAR, and TFCAS between 2016-2022. Chi-square and logistic regression modeling analyzed outcomes including in-hospital stroke, death, MI, and 30-day mortality by weekend vs. weekday intervention. Backward stepwise regression was utilized to identify significant confounding variables and were ultimately included in each final logistic regression model. Logistic regression of outcomes was substratified by symptomatic status. Secondary multivariable analysis compared outcomes between the three revascularization methods by weekend vs. weekday interventions.
RESULTS
155,962 procedures were analyzed including 103,790 CEA, 31,666 TCAR and 20,506 TFCAS. Of these, 1988 CEA, 246 TCAR and 820 TFCAS received weekend interventions. Logistic regression demonstrated no significant differences for TCAR, and increased odds of in-hospital stroke/death/MI for CEA [OR:1.31,(1.04-1.65)] and TFCAS [OR:1.46,(1.09-1.96)] weekend procedures. Asymptomatic TCAR patients had nearly triple the odds of 30-day mortality [OR:2.85,(1.06-7.68), P=0.038]. Similarly, odds of in-hospital death were nearly tripled for asymptomatic CEA [OR:2.89,(1.30-6.43), P=0.009] and asymptomatic TFCAS [OR:2.78,(1.34-5.76), P=0.006] patients. Secondary analysis demonstrated that CEA and TCAR had no significant differences for all outcomes. TFCAS was associated with increased odds of stroke and death compared to CEA and TCAR.
CONCLUSION
In this observational cohort study, we found that weekend carotid revascularization is associated with increased odds of complications and mortality. Furthermore, asymptomatic weekend patients perform worse in the CEA and TFCAS procedural groups. Among the three revascularization methods, TFCAS is associated with the highest odds of perioperative stroke and mortality. As such, our findings suggest that TFCAS procedures should be avoided over the weekend, in favor of CEA or TCAR. In patients who are poor candidates for CEA, TCAR offers the lowest morbidity and mortality for weekend procedures.
PubMed: 38942398
DOI: 10.1016/j.jvs.2024.06.163 -
The Spine Journal : Official Journal of... Jun 2024Associations between magnetic resonance imaging (MRI)-detected lumbar intervertebral disc degeneration (LDD) and LBP are often of modest magnitude. This association may...
BACKGROUND CONTEXT
Associations between magnetic resonance imaging (MRI)-detected lumbar intervertebral disc degeneration (LDD) and LBP are often of modest magnitude. This association may be larger in specific patient subgroups.
PURPOSE
To examine whether the association between LDD and LBP is modified by underlying genetic predispositions to pain.
STUDY DESIGN
Cross-sectional study in UK Biobank (UKB) and TwinsUK.
PATIENT SAMPLES
A genome-wide association study (GWAS) of the number of anatomical chronic pain locations was conducted in 347,538 UKB participants. The GWAS was used to develop a genome-wide polygenic risk score (PRS) in a holdout sample of 30,000 UKB participants. The PRS model was then used in analyses of 645 TwinsUK participants with standardized LDD MRI assessments.
OUTCOME MEASURES
Ever having had LBP associated with disability lasting ≥1 month (LBP1).
METHODS
Using the PRS as a proxy for "genetically-predicted propensity to pain", we stratified TwinsUK participants into PRS quartiles. A "basic" model examined the association between an LDD summary score (LSUM) and LBP1, adjusting for covariates. A "fully-adjusted" model also adjusted for PRS quartile and LSUM x PRS quartile interaction terms.
RESULTS
In the basic model, the odds ratio (OR) of LBP1 was 1.8 per standard deviation of LSUM (95% confidence interval [CI] 1.4 -2.3). In the fully-adjusted model, there was a statistically significant LSUM-LBP1 association in quartile 4, the highest PRS quartile (OR = 2.5 [95% CI 1.7-3.7], p=2.6×10), and in quartile 3 (OR=2.0, [95% CI 1.3-3.0]; p=0.002), with small-magnitude and/or non-significant associations in the lowest two PRS quartiles. PRS quartile was a significant effect modifier of the LSUM-LBP1 association (interaction p≤0.05).
CONCLUSIONS
Genetically-predicted propensity to pain modifies the LDD-LBP association, with the strongest association present in people with the highest genetic propensity to pain. Lumbar MRI findings may have stronger connections to LBP in specific subgroups of people.
PubMed: 38942297
DOI: 10.1016/j.spinee.2024.05.018 -
NeuroImage Jun 2024The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has...
BACKGROUND
The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI).
METHODS
We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques.
RESULTS
Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures.
CONCLUSION
This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.
PubMed: 38942101
DOI: 10.1016/j.neuroimage.2024.120695 -
Medical Image Analysis Jun 2024Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it...
Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.
PubMed: 38941858
DOI: 10.1016/j.media.2024.103231 -
Scientific Reports Jun 2024We aimed to identify the clinical subtypes in individuals starting long-term care in Japan and examined their association with prognoses. Using linked medical insurance...
We aimed to identify the clinical subtypes in individuals starting long-term care in Japan and examined their association with prognoses. Using linked medical insurance claims data and survey data for care-need certification in a large city, we identified participants who started long-term care. Grouping them based on 22 diseases recorded in the past 6 months using fuzzy c-means clustering, we examined the longitudinal association between clusters and death or care-need level deterioration within 2 years. We analyzed 4,648 participants (median age 83 [interquartile range 78-88] years, female 60.4%) between October 2014 and March 2019 and categorized them into (i) musculoskeletal and sensory, (ii) cardiac, (iii) neurological, (iv) respiratory and cancer, (v) insulin-dependent diabetes, and (vi) unspecified subtypes. The results of clustering were replicated in another city. Compared with the musculoskeletal and sensory subtype, the adjusted hazard ratio (95% confidence interval) for death was 1.22 (1.05-1.42), 1.81 (1.54-2.13), and 1.21 (1.00-1.46) for the cardiac, respiratory and cancer, and insulin-dependent diabetes subtypes, respectively. The care-need levels more likely worsened in the cardiac, respiratory and cancer, and unspecified subtypes than in the musculoskeletal and sensory subtype. In conclusion, distinct clinical subtypes exist among individuals initiating long-term care.
Topics: Humans; Female; Aged; Male; Japan; Cluster Analysis; Aged, 80 and over; Long-Term Care; Prognosis; Neoplasms
PubMed: 38942898
DOI: 10.1038/s41598-024-65699-6 -
Europace : European Pacing,... Jun 2024To describe the rationale, design, delivery and baseline characteristics of STEEER-AF (Stroke prevention and rhythm control Treatment: Evaluation of an Educational...
Design and deployment of the STEEER-AF trial to evaluate and improve guideline adherence: A cluster-randomised trial by the European Society of Cardiology and European Heart Rhythm Association.
AIMS
To describe the rationale, design, delivery and baseline characteristics of STEEER-AF (Stroke prevention and rhythm control Treatment: Evaluation of an Educational programme of the European Society of Cardiology [ESC] in a cluster-Randomised trial in patients with Atrial Fibrillation).
METHODS & RESULTS
STEEER-AF is a pragmatic trial designed to objectively and robustly determine whether guidelines are adhered to in routine practice, and evaluate a targeted educational programme for healthcare professionals. Seventy centres were randomised in 6 countries (France, Germany, Italy, Poland, Spain and United Kingdom; 2022-2023). STEEER-AF centres recruited 1732 patients with a diagnosis of atrial fibrillation (AF), with mean age 68.9 years (SD 11.7), CHA2DS2-VASc score 3.2 (SD 1.8) and 647 (37%) women. 843 patients (49%) were in AF and 760 (44%) in sinus rhythm at enrolment. Oral anticoagulant therapy was prescribed in 1,543 patients (89%), with the majority receiving direct oral anticoagulants (1,378; 89%). Previous cardioversion, antiarrhythmic drug therapy or ablation was recorded in 836 patients (48.3%). 551 patients (31.8%) were currently receiving an antiarrhythmic drug, and 446 (25.8%) were scheduled to receive a future cardioversion or ablation. The educational programme engaged 195 healthcare professionals across centres randomised to the intervention group, consisting of bespoke interactive online learning and reinforcement activities, supported by national expert trainers.
CONCLUSION
The STEEER-AF trial was successfully deployed across six European countries to investigate guideline adherence in real-world practice, and evaluate if a structured educational programme for healthcare professionals can improve patient-level care.
REGISTRATION
Clinicaltrials.gov NCT04396418.
PubMed: 38940494
DOI: 10.1093/europace/euae178 -
Aging & Mental Health Jun 2024To examine (1) how visual green space quantity and quality affect depression among older adults; (2) whether and how the links may be mediated by perceived stress,...
OBJECTIVES
To examine (1) how visual green space quantity and quality affect depression among older adults; (2) whether and how the links may be mediated by perceived stress, physical activity, neighbourhood social cohesion, and air pollution (PM); and (3) whether there are differences in the mediation across visual green space quantity and quality.
METHOD
We used older adults samples (aged over 65) from the WHO Study on Global Ageing and Adult Health in Shanghai, China. Depression was quantified by two self-reported questions related to the diagnosis of depression and medications or other treatments for depression. Visual green space quantity and quality were calculated using street view images and machine learning methods (street view green space = SVG). Mediators included perceived stress, social cohesion, physical activity, and PM. Multilevel logistic and linear regression models were applied to understand the mediating roles of the above mediators in the link between visual green space quantity and quality and depression in older adults.
RESULTS
SVG quantity and quality were negatively related to depression. Significant partial mediators for SVG quality were social cohesion and perceived stress. For SVG quantity, there was no evidence that any of the above mediators mediated the association.
CONCLUSION
Our results indicated that visual green space quantity and quality may be related to depression in older adults through different mechanisms.
PubMed: 38940438
DOI: 10.1080/13607863.2024.2363370