The vocabulary gap between consumers and professionals in the medical domain hinders information seeking and communication. Consumer health vocabularies have been developed to aid such informatics applications. This purpose is best served if the vocabulary evolves with consumers' language.

To investigate the treatment effect of algae biosorbent on heavy metal wastewater, in this paper, the adsorption effect of M. aeruginosa powder on heavy metal ions copper, cadmium and nickel was investigated using the uniform experimental method, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and TG-DSC comprehensive thermal analysis. The experimental results showed that the initial concentration of copper ion solution was 25 mg/L, the temperature was 30 °C, the pH value was 8 and the adsorption time was 5 h, which was the best condition for the removal of copper ions by algae powder adsorption, and the removal rate was 83.24%. The initial concentration of cadmium ion solution was 5 mg/L, the temperature was 35 °C, the pH value was 8 and the adsorption time was 4 h, which was the best condition for the adsorption of cadmium ion by algae powder, and the removal rate was 92.00%. The initial nickel ion solution concentration of 15 mg/L, temperature of 35 °C, pH value of 7 and adsorption time of 1 h were the best conditions for the adsorption of nickel ions by algae powder, and the removal rate was 88.67%. The spatial structure of algae powder changed obviously before and after adsorbing heavy metals. The functional groups such as amino and phosphate groups on the cell wall of M. aeruginosa enhanced the adsorption effect of heavy metal ions copper, cadmium and nickel. Additionally, M. aeruginosa adsorption of heavy metal ions copper, cadmium, nickel is an exothermic process. The above experiments show that M. aeruginosa can be used as a biological adsorbent to remove heavy metals, which lays a theoretical foundation for the subsequent treatment of heavy metal pollution by algae.

Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions.

Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction).

Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.

We conducted a functional magnetic resonance imaging (fMRI) study and a voxel-based morphometry (VBM) study to investigate the functional and structural basis of how visual search for flavor labels in packaging is influenced by the color of the packaging. The participants were cued by a flavor word before searching for a package with this flavor label. The behavioral results of both studies revealed that the participants were slower to find the target when its color was incongruent with the flavor label in terms of color-flavor associations than when it was congruent with the flavor label, which is indicative of a color-flavor incongruency effect in the reaction times. The fMRI results revealed that this behavioral effect was associated with enhanced activation in the right putamen. The VBM results further revealed a significant positive correlation between the magnitude of the behavioral effect and volume of gray matter in the right putamen. Taken together, these findings suggest that the color-flavor incongruency effect may be attributed to the violation of color expectation in the incongruent condition and that the putamen may be one of the important areas for processing events in violation of expectation.

Understanding semantic relatedness and similarity between biomedical terms has a great impact on a variety of applications such as biomedical information retrieval, information extraction, and recommender systems. The objective of this study is to examine word2vec's ability in deriving semantic relatedness and similarity between biomedical terms from large publication data. Specifically, we focus on the effects of recency, size, and section of biomedical publication data on the performance of word2vec.

A more effective and better-tolerated site for repetitive transcranial magnetic stimulation (rTMS) for treating cognitive dysfunction in patients with bipolar disorder (BD) is needed. The primary visual cortex (V1) may represent a suitable site. To investigate the use of the V1, which is functionally linked to the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), as a potential site for improving cognitive function in BD. Seed-based functional connectivity (FC) analysis was used to locate targets in the V1 that had significant FC with the DLPFC and ACC. Subjects were randomly assigned to 4 groups, namely, the DLPFC active-sham rTMS (A1), DLPFC sham-active rTMS (A2), ACC active-sham rTMS (B1), and ACC sham-active rTMS groups (B2). The intervention included the rTMS treatment once daily, with five treatments a week for four weeks. The A1 and B1 groups received 10 days of active rTMS treatment followed by 10 days of sham rTMS treatment. The A2 and B2 groups received the opposite. The primary outcomes were changes in the scores of five tests in the THINC-integrated tool (THINC-it) at week 2 (W2) and week 4 (W4). The secondary outcomes were changes in the FC between the DLPFC/ACC and the whole brain at W2 and W4. Of the original 93 patients with BD recruited, 86 were finally included, and 73 finished the trial. Significant interactions between time and intervention type (Active/Sham) were observed in the scores of the accuracy of the Symbol Check in the THINC-it tests at baseline (W0) and W2 in groups B1 and B2 (F = 4.736, p = 0.037) using a repeated-measures analysis of covariance approach. Group B1 scored higher in the accuracy of Symbol Check at W2 compared with W0 (p < 0.001), while the scores of group B2 did not differ significantly between W0 and W2. No significant interactions between time and intervention mode were seen between groups A1 and A2, nor was any within-group significance of FC between DLPFC/ACC and the whole brain observed between baseline (W0) and W2/W4 in any group. One participant in group B1 experienced disease progression after 10 active and 2 sham rTMS sessions. The present study demonstrated that V1, functionally correlated with ACC, is a potentially effective rTMS stimulation target for improving neurocognitive function in BD patients. Further investigation using larger samples is required to confirm the clinical efficacy of TVCS.

During the persistent COVID-19 pandemic, the swift progression of acute myocarditis has emerged as a profound concern due to its augmented mortality, underscoring the urgency of prompt diagnosis. This study analyzed blood samples from 5,230 COVID-19 individuals, identifying key blood and myocardial markers that illuminate the relationship between COVID-19 severity and myocarditis. A predictive model, applying Bayesian and random forest methodologies, was constructed for myocarditis' early identification, unveiling a balanced gender distribution in myocarditis cases contrary to a male predominance in COVID-19 occurrences. Particularly, older men exhibited heightened vulnerability to severe COVID-19 strains. The analysis revealed myocarditis was notably prevalent in younger demographics, and two subvariants COVID-19 progression paths were identified, characterized by symptom intensity and specific blood indicators. The enhanced myocardial marker model displayed remarkable diagnostic accuracy, advocating its valuable application in future myocarditis detection and treatment strategies amidst the COVID-19 crisis.

Plant mitochondrial transcription termination factor (mTERF) family regulates organellar gene expression (OGE) and is functionally characterized in diverse species. However, limited data are available about its functions in the agriculturally important cereal barley (Hordeum vulgare L.). In this study, we identified 60 mTERFs in the barley genome (HvmTERFs) through a comprehensive search against the most updated barley reference genome, Morex V2. Then, phylogenetic analysis categorized these genes into nine subfamilies, with approximately half of the HvmTERFs belonging to subfamily IX. Members within the same subfamily generally possessed conserved motif composition and exon-intron structure. Both segmental and tandem duplication contributed to the expansion of HvmTERFs, and the duplicated gene pairs were subjected to strong purifying selection. Expression analysis suggested that many HvmTERFs may play important roles in barley development (e.g., seedlings, leaves, and developing inflorescences) and abiotic stresses (e.g., cold, salt, and metal ion), and HvmTERF21 and HvmTERF23 were significant induced by various abiotic stresses and/or phytohormone treatment. Finally, the nucleotide diversity was decreased by only 4.5% for HvmTERFs during the process of barley domestication. Collectively, this is the first report to characterize HvmTERFs, which will not only provide important insights into further evolutionary studies but also contribute to a better understanding of the potential functions of HvmTERFs and ultimately will be useful in future gene functional studies.

HOTTIP lncRNA is highly expressed in acute myeloid leukemia (AML) driven by MLL rearrangements or NPM1 mutations to mediate HOXA topologically associated domain (TAD) formation and drive aberrant transcription. However, the mechanism through which HOTTIP accesses CCCTC-binding factor (CTCF) chromatin boundaries and regulates CTCF-mediated genome topology remains unknown. Here, we show that HOTTIP directly interacts with and regulates a fraction of CTCF-binding sites (CBSs) in the AML genome by recruiting CTCF/cohesin complex and R-loop-associated regulators to form R-loops. HOTTIP-mediated R-loops reinforce the CTCF boundary and facilitate formation of TADs to drive gene transcription. Either deleting CBS or targeting RNase H to eliminate R-loops in the boundary CBS of β-catenin TAD impaired CTCF boundary activity, inhibited promoter/enhancer interactions, reduced β-catenin target expression, and mitigated leukemogenesis in xenograft mouse models with aberrant HOTTIP expression. Thus, HOTTIP-mediated R-loop formation directly reinforces CTCF chromatin boundary activity and TAD integrity to drive oncogene transcription and leukemia development.