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Biomedical Reports Aug 2024Type 2 diabetes mellitus (T2DM) is a major global health problem. Response to first-line therapy is variable. This is partially due to interindividual variability across...
Type 2 diabetes mellitus (T2DM) is a major global health problem. Response to first-line therapy is variable. This is partially due to interindividual variability across those genes codifying transport, metabolising, and drug activation proteins involved in first-line pharmacological treatment. Single nucleotide polymorphisms (SNPs) of genes and affect metformin therapeutic response in patients with T2DM patients. The present study investigated allelic and genotypic frequencies of organic cation (OCT)1, OCT2, and OCT3 polymorphisms among metformin-treated patients with type 2 diabetes mellitus (T2DM). It also reports the association between clinical and genetic variables with glycated haemoglobin (HbA1c) control in 59 patients with T2DM. Patients were genotyped through real-time PCR (TaqMan assays). Metformin plasmatic levels were determined by mass spectrometry. Neither the analysis of HbA1c control by SNPs in , and , nor the dominant genotypic model analysis yielded statistical significance between genotypes in polymorphisms rs72552763 (P=0.467), rs622342 (P=0.221), rs316019 (P=0.220) and rs2076828 (P=0.215). HbA1c levels were different in rs72552763 [GAT/GAT, 6.0 (5.7-6.6), GAT/del=6.5 (6.2-9.0), del/del=6.5 (6.4-6.8); P=0.022] and rs622342 [A/A=6.0 (5.8-6.5), A/C=6.4 (6.1-7.7), C/C=6.8 (6.4-9.3); P=0.009] genotypes. The dominant genotypic model found the lowest HbA1c levels in GAT/GAT (P=0.005) and A/A (P=0.010), in rs72552763 (GAT/GAT vs. GAT/del + del/del) and rs622342 (A/A vs. A/C + CC), respectively. There was a significant correlation between HbA1c levels and metformin dosage amongst del allele carriers in rs72552763 (β=0.14, P<0.001, r=0.387), as opposed to GAT/GAT in rs72552763. There were no differences between HbA1c values in the test set and those predicted by machine learning models employing a simple linear regression based on metformin dosage. Therefore, rs72552763 and rs622342 polymorphisms in may affect metformin response determined by HbA1c levels in patients with T2DM. The del allele of SNP rs72552763 may serve as a metformin response biomarker.
PubMed: 38938740
DOI: 10.3892/br.2024.1806 -
Journal of Extracellular Biology Jan 2024Extracellular vesicles (EVs) are membranous structures released by cells into the extracellular space and are thought to be involved in cell-to-cell communication. While...
Extracellular vesicles (EVs) are membranous structures released by cells into the extracellular space and are thought to be involved in cell-to-cell communication. While EVs and their cargo are promising biomarker candidates, sorting mechanisms of proteins to EVs remain unclear. In this study, we ask if it is possible to determine EV association based on the protein sequence. Additionally, we ask what the most important determinants are for EV association. We answer these questions with explainable AI models, using human proteome data from EV databases to train and validate the model. It is essential to correct the datasets for contaminants introduced by coarse EV isolation workflows and for experimental bias caused by mass spectrometry. In this study, we show that it is indeed possible to predict EV association from the protein sequence: a simple sequence-based model for predicting EV proteins achieved an area under the curve of 0.77 ± 0.01, which increased further to 0.84 ± 0.00 when incorporating curated post-translational modification (PTM) annotations. Feature analysis shows that EV-associated proteins are stable, polar, and structured with low isoelectric point compared to non-EV proteins. PTM annotations emerged as the most important features for correct classification; specifically, palmitoylation is one of the most prevalent EV sorting mechanisms for unique proteins. Palmitoylation and nitrosylation sites are especially prevalent in EV proteins that are determined by very strict isolation protocols, indicating they could potentially serve as quality control criteria for future studies. This computational study offers an effective sequence-based predictor of EV associated proteins with extensive characterisation of the human EV proteome that can explain for individual proteins which factors contribute to their EV association.
PubMed: 38938677
DOI: 10.1002/jex2.120 -
JACC. Advances Dec 2023
PubMed: 38938477
DOI: 10.1016/j.jacadv.2023.100682 -
Aging & Mental Health Jun 2024Support for people with dementia in their communities is neither robust nor consistent in the UK, often bolstered by third sector/grass-roots initiatives facing...
OBJECTIVES
Support for people with dementia in their communities is neither robust nor consistent in the UK, often bolstered by third sector/grass-roots initiatives facing formidable challenges in sustaining long-term. The Get Real with Meeting Centres project explored factors involved in sustaining one such form of community-based support. This is the second of two linked articles outlining learning from this realist evaluation of Meeting Centres (MCs) for people with dementia and carers, which focusses on findings regarding their operational and strategic running.
METHOD
Semi-structured interviews and focus group discussions were conducted with 77 participants across three MC sites in England and Wales, including people living with dementia, informal carers, staff, volunteers, trustees, and supporting professionals/practitioners. Data were themed, then analysed using soft systems methodology and realist logic of analysis.
RESULTS
Forty-two 'context-mechanism-outcome' statements were generated, explaining how background circumstances might trigger responses/processes to produce wanted or unwanted outcomes regarding three key areas for MC sustainability: and
CONCLUSION
Collaboration is essential to sustaining community-based initiatives such as MCs, particularly between local community and regional level. MCs need to be vigilant in mitigating pressures that create 'mission drift', as targeting a gap in the care pathway and maintaining a person-centred ethos are central to MCs' appeal. Stable, ongoing funding is needed for stable, ongoing community dementia support. More formal recognition of the value of social model community-based initiatives, helped by improved data collection, would encourage more robust and consistent community dementia support.
PubMed: 38938166
DOI: 10.1080/13607863.2024.2372058 -
Behavior Therapy Jul 2024Prior research has demonstrated that conducting acquisition in multiple contexts results in more responding to the point that it can even nullify the benefit of...
Prior research has demonstrated that conducting acquisition in multiple contexts results in more responding to the point that it can even nullify the benefit of subsequent extinction in multiple contexts on reducing renewal of excitatory responding. The underlying mechanism to explain why this happens has not been systematically examined. Using self-reported expectancy of the outcome, the current study investigates three mechanisms that potentially explain why acquisition in multiple contexts results in more responding-greater generalization, stronger acquisition learning, or slower extinction learning. Participants (N = 180) received discriminative training with a conditioned stimulus (CS+) and outcome pairing and a CS- → noOutcome pairing in either one or three contexts. This was followed by either extinction treatment in a novel context or no extinction. Finally, testing occurred in the acquisition context, the extinction context, or a novel context. Stronger renewal of extinguished conditioned expectation was observed for participants who received CS+ → Outcome pairings in three contexts relative to one context. There was no effect of the number of contexts on the strength of the excitatory CS+ → Outcome association or degree of inhibitory learning that occurred during extinction. This suggests that generalization is the mechanism responsible for the adverse impact to extinction learning when acquisition is conducted in multiple contexts.
Topics: Humans; Extinction, Psychological; Generalization, Psychological; Male; Female; Young Adult; Conditioning, Classical; Adult; Adolescent; Discrimination Learning
PubMed: 38937046
DOI: 10.1016/j.beth.2023.10.004 -
Diabetes Research and Clinical Practice Jun 2024Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the...
BACKGROUND
Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices METHODS: This was a prospective cohort study comparing people with (n = 1256) and without (n = 5193) diabetes mellitus in phase II of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. The association of several anthropometric indices in phase I, including Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Visceral Adiposity Index (VAI), Weight-Adjusted-Waist Index (WWI), Body Roundness Index (BRI), Body Surface Area (BSA), Conicity Index (C-Index) and Lipid Accumulation Product (LAP) with T2DM incidence (in phase II) were examined; using Logistic Regression (LR) and Decision Tree (DT) analysis.
RESULTS
BMI followed by VAI and LAP were the best predictors of T2DM incidence. Participants with BMI < 21.25 kg/m and VAI ≤ 5.9 had a lower chance of diabetes than those with higher BMI and VAI levels (0.033 vs. 0.967 incident rate). For BMI > 25 kg/m, the chance of diabetes rapidly increased (OR = 2.27).
CONCLUSIONS
BMI, VAI, and LAP were the best predictors of T2DM incidence.
PubMed: 38936481
DOI: 10.1016/j.diabres.2024.111755 -
Nurse Education in Practice Jun 2024This study explores and describes the second victim phenomenon in nursing students in association with the characteristics of the clinical learning environment and the...
AIM
This study explores and describes the second victim phenomenon in nursing students in association with the characteristics of the clinical learning environment and the clinical supervision process.
DESIGN
Qualitative design using conventional content analysis and summative content analysis approaches.
METHODS
From September 2022 to July 2023, in-depth semi-structured individual interviews were conducted with a purposive sample of 10 undergraduate nursing students.
RESULTS
Six main themes were developed: 'defining the physical and psychological responses after the most significant patient safety incident', 'analyzing the characteristics of patient safety incidents', 'creating a safe learning environment to provide the best care for patients', 'developing mentorship capabilities and qualities for an ideal follow up of students as a second victim', 'providing resources and integrating support structures to second victim nursing students during their clinical learning', and 'considering the cooperation and coordination between the health institution and the higher education institutions.'
CONCLUSION
Nursing students become second victims during their clinical placement. The clinical learning environment and mentoring characteristics influence the second victim experience.
PubMed: 38936299
DOI: 10.1016/j.nepr.2024.104038 -
Computers in Biology and Medicine Jun 2024Biomedical knowledge graphs (KGs) serve as comprehensive data repositories that contain rich information about nodes and edges, providing modeling capabilities for...
Biomedical knowledge graphs (KGs) serve as comprehensive data repositories that contain rich information about nodes and edges, providing modeling capabilities for complex relationships among biological entities. Many approaches either learn node features through traditional machine learning methods, or leverage graph neural networks (GNNs) to directly learn features of target nodes in the biomedical KGs and utilize them for downstream tasks. Motivated by the pre-training technique in natural language processing (NLP), we propose a framework named PT-KGNN (Pre-Training the biomedical KG with GNNs) to learn embeddings of nodes in a broader context by applying GNNs on the biomedical KG. We design several experiments to evaluate the effectivity of our proposed framework and the impact of the scale of KGs. The results of tasks consistently improve as the scale of the biomedical KG used for pre-training increases. Pre-training on large-scale biomedical KGs significantly enhances the drug-drug interaction (DDI) and drug-disease association (DDA) prediction performance on the independent dataset. The embeddings derived from a larger biomedical KG have demonstrated superior performance compared to those obtained from a smaller KG. By applying pre-training techniques on biomedical KGs, rich semantic and structural information can be learned, leading to enhanced performance on downstream tasks. it is evident that pre-training techniques hold tremendous potential and wide-ranging applications in bioinformatics.
PubMed: 38936076
DOI: 10.1016/j.compbiomed.2024.108768 -
Journal of American College Health : J... Jun 2024The COVID-19 pandemic caused severe disruptions in living and learning to millions of college students. Here we investigated using mediation analysis two dimensions of...
The COVID-19 pandemic caused severe disruptions in living and learning to millions of college students. Here we investigated using mediation analysis two dimensions of anxiety that were specific to the pandemic - COVID-19 related anxiety and COVID-19 vaccine anxiety - to evaluate their relationship to college adjustment during the pandemic. Using cross-sectional survey data across three semester waves (Spring 2021, Fall 2021, and Spring 2022) we probed whether anxiety functioned as a challenge or hindrance stressor on adjustment. We found that although anxiety decreased in both COVID-19 dimensions across semesters, student adjustment to college remained consistently low. Our mediation analysis revealed that both COVID-19 related anxiety and COVID-19 vaccine-related anxiety functioned as challenge stressors, elevating academic, social, personal-emotional, and institutional adjustment during the pandemic. We discuss the role of positive COVID impacts on college adjustment, including enhanced social support.
PubMed: 38935576
DOI: 10.1080/07448481.2024.2362322 -
Optometry and Vision Science : Official... Jun 2024Our retinal image-based deep learning (DL) cardiac biological age (BioAge) model could facilitate fast, accurate, noninvasive screening for cardiovascular disease (CVD)...
SIGNIFICANCE
Our retinal image-based deep learning (DL) cardiac biological age (BioAge) model could facilitate fast, accurate, noninvasive screening for cardiovascular disease (CVD) in novel community settings and thus improve outcome with those with limited access to health care services.
PURPOSE
This study aimed to determine whether the results issued by our DL cardiac BioAge model are consistent with the known trends of CVD risk and the biomarker leukocyte telomere length (LTL), in a cohort of individuals from the UK Biobank.
METHODS
A cross-sectional cohort study was conducted using those individuals in the UK Biobank who had LTL data. These individuals were divided by sex, ranked by LTL, and then grouped into deciles. The retinal images were then presented to the DL model, and individual's cardiac BioAge was determined. Individuals within each LTL decile were then ranked by cardiac BioAge, and the mean of the CVD risk biomarkers in the top and bottom quartiles was compared. The relationship between an individual's cardiac BioAge, the CVD biomarkers, and LTL was determined using traditional correlation statistics.
RESULTS
The DL cardiac BioAge model was able to accurately stratify individuals by the traditional CVD risk biomarkers, and for both males and females, those issued with a cardiac BioAge in the top quartile of their chronological peer group had a significantly higher mean systolic blood pressure, hemoglobin A1c, and 10-year Pooled Cohort Equation CVD risk scores compared with those individuals in the bottom quartile (p<0.001). Cardiac BioAge was associated with LTL shortening for both males and females (males: -0.22, r2 = 0.04; females: -0.18, r2 = 0.03).
CONCLUSIONS
In this cross-sectional cohort study, increasing CVD risk whether assessed by traditional biomarkers, CVD risk scoring, or our DL cardiac BioAge, CVD risk model, was inversely related to LTL. At a population level, our data support the growing body of evidence that suggests LTL shortening is a surrogate marker for increasing CVD risk and that this risk can be captured by our novel DL cardiac BioAge model.
PubMed: 38935034
DOI: 10.1097/OPX.0000000000002158