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Influence of ABCB1 polymorphisms on aripiprazole and dehydroaripiprazole plasma concentrations
AbstractAripiprazole (ARI) is an atypical antipsychotic which is a substrate of P-glycoprotein (P-gp), a transmembrane glycoprotein that plays a crucial role in eliminating potentially harmful compounds from the organism. ARI once-monthly (AOM) is a long-acting injectable form which improves treatment compliance. Genetic polymorphisms in ABCB1 may lead to changes in P-gp function, leading to individual differences in drug disposition. The present study aims to determine how the different variants of the three most prevalent SNPs of the ABCB1 gene affect plasma concentrations of ARI, of its active metabolite dehydroaripiprazole (DHA) and ARI/DHA ratio in patients under AOM treatment. The metabolizing state of the two main aripiprazole metabolizing enzymes (CYP2D6 and CYP3A4) were considered to specifically study the effect of P-gp on plasma concentrations of the parent compound and its active metabolite. The study found a clear relationship between the genotypes found for the different ABCB1 SNPs and the ARI/DHA ratio. Specifically, patients with GG genotype in G2677T have almost twice the ratio compared to TT genotype. Similarly, this increase is also found in C3435T with 1.4-fold and in C1236T with 1.6-fold for the same genotypes. Regarding haplotypes, significant differences were obtained between CC-GG-CC and TT-TT-TT patients, with an 87.9% higher ratio in patients with the CC-GG-CC haplotype. There was a clear trend towards lower ARI concentrations and higher DHA concentrations when the presence of mutated T alleles increases. The ABCB1 gene could be a good partner along with CYP2D6 and CYP3A4 genotyping in conjunction with monitoring ARI plasma concentrations.
Data-driven prediction of chemically relevant compositions in multi-component systems using tensor embeddings
AbstractThe discovery of novel materials is crucial for developing new functional materials. This study introduces a predictive model designed to forecast complex multi-component oxide compositions, leveraging data derived from simpler pseudo-binary systems. By applying tensor decomposition and machine learning techniques, we transformed pseudo-binary oxide compositions from the Inorganic Crystal Structure Database (ICSD) into tensor representations, capturing key chemical trends such as oxidation states and periodic positions. Tucker decomposition was utilized to extract tensor embeddings, which were used to train a Random Forest classifier. The model successfully predicted the existence probabilities of pseudo-ternary and quaternary oxides, with 84% and 52% of ICSD-registered compositions, respectively, achieving high scores. Our approach highlights the potential of leveraging simpler oxide data to predict more complex compositions, suggesting broader applicability to other material systems such as sulfides and nitrides.
Who’s quitting academia? Data reveal gender gaps in surprising fields
Overburden movement law in strip filling mining of upward mining faces
Why the last cases of sleeping sickness will be the hardest to eliminate
Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis
AbstractAutomated tools for quantification of idiopathic pulmonary fibrosis (IPF) can aid in ensuring reproducibility, however their complexity and costs can differ substantially. In this retrospective study, two automated tools were compared in 45 patients with biopsy proven (12/45) and imaging-based (33/45) IPF diagnosis (mean age 74 ± 9 years, 37 male) for quantification of pulmonary fibrosis in CT. First, a tool that identifies multiple characteristic lung texture features was applied to measure multi-texture fibrotic lung (MTFL) by combining the amount of ground glass, reticulation, and honeycombing. Opacity-based fibrotic lung (OFL) was measured by a second tool that performs a simpler binary classification of tissue into either normal or opacified lung and was originally developed for quantifying pneumonia. Differences in quantification of MTFL and OFL were assessed by Mann-Whitney U-test and Pearson correlation (r). Also, correlation with spirometry parameters (percent predicted total lung capacity (TLC), percent predicted vital capacity (VC), percent predicted forced expiratory volume in 1 s (FEV1), diffusing capacity of the lungs for carbon monoxide (DLCO), partial pressure of oxygen (PO2) and carbon dioxide (PCO2)) were assessed by r. The prognostic values for 3-year patient survival of OFL, LSS and MTFL were investigated by multivariable Cox-proportional-hazards (CPH) models including sex, age and TLC and including sex, age and VC. Also, Kaplan-Meier analysis with log rank test between subgroups separated by median OFL and MTFL were conducted. No significant difference between OFL and MTFL was observed (median and interquartile range: OFL = 29% [20-38%], MTFL = 31% [19-45%]; P = 0.44). For OFL significant correlation was observed to MTFL (r = 0.93, P < 0.01) and VC (r=-0.50, P = 0.03). For MTFL no significant correlation to spirometry parameters was found. The total time for one analysis was lower for the automated MTFL (MTFL: 313 ± 25s vs. OFL: 612 ± 61s, P < 0.001). Both analyses were significant predictors in the multivariable CPH analysis including TLC (hazard-ratios: MTFL 1.03 [1.01–1.06], P = 0.02; OFL 1.03 [1.00-1.06], P = 0.03). No parameter was a significant predictor in the CPH models including VC (hazard-ratios: MTFL 1.01 [0.98–1.04], P = 1; OFL 1.01 [0.97–1.05], P = 1). OFL showed significance in Kaplan-Meier analysis (MTFL: P = 0.17; OFL: P = 0.03). Using a simple opacity-based quantification of pulmonary fibrosis in IPF patients displayed similar results and prognostic value compared to a more complex multi-texture based analysis.
The concomitant anxiety and depression in patients with epilepsy caused by low-grade brain tumors
Utilization of telemedicine in conjunction with wearable devices for patients with chronic musculoskeletal pain: a randomized controlled clinical trial
Daily briefing: Meet Punk and Emo, the 430-million-year old mollusc fossils
Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement
Revealed: the fatty cells that are the ‘bubble wrap’ of the body
Asymptomatic infection and antibody prevalence to co-occurring avian influenza viruses vary substantially between sympatric seabird species following H5N1 outbreaks
Abstract Emerging infectious diseases are of major concern to animal and human health. Recent emergence of high pathogenicity avian influenza virus (HPAIV) (H5N1 clade 2.3.4.4b) led to substantial global mortality across a range of host species. Co-occurring species showed marked differences in mortality, generating an urgent need for better epidemiological understanding within affected populations. We therefore tested for antibodies, indicative of previous exposure and recovery, and for active viral infection in apparently healthy individuals (n = 350) across five co-occurring seabird species on the Isle of May, Scotland, during 2023, following H5N1 HPAIV associated mortality in the preceding summer. Antibody prevalence to AIV subtypes varied substantially between species, ranging from 1.1% in European shags ( Gulosus aristotelis ) (to H5) to 78.7% in black-legged kittiwakes ( Rissa tridactyla ) (to H16 or both H13 and H16), and between 31 and 41% for three auk species (H5, H16 or both). At least 20.4% of auks had antibodies to an as yet unidentified subtype, suggesting further subtypes circulating in the population. We found low levels of active, but asymptomatic, AIV infection in individuals (1.6–4.5%), but excluded this as H5N1. Our results emphasise the importance of testing healthy individuals to understand the prevalence of co-circulating AIV subtypes in wild populations, and the potential for future reassortment events which could alter virus behaviour and impact.
Sugemule-7 alleviates oxidative stress, neuroinflammation, and cell death, promoting synaptic plasticity recovery in mice with postpartum depression
Characterising the change rule of freshness and inorganic anions in reconstituted tobacco pulp with oscillation time
Reinventing type 2 immunity in cancer
The influence of architectural heritage and tourists’ positive emotions on behavioral intentions using eye-tracking study
Assessing the preservation potential of successive hurricane overwash deposits in Florida, USA mangroves
Accurate predictions on small data with a tabular foundation model
AbstractTabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science1,2. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories3–5, gradient-boosted decision trees6–9 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8 s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains.