Molecular landscapes of human hippocampal immature neurons across lifespan.

Immature dentate granule cells (imGCs) arising from adult hippocampal neurogenesis contribute to plasticity and unique brain functions in rodents1,2 and are dysregulated in multiple human neurological disorders3-5. Little is known about the molecular characteristics of adult human hippocampal imGCs, and even their existence is under debate1,6-8. Here we performed single-nucleus RNA sequencing aided by a validated machine learning-based analytic approach to identify imGCs and quantify their abundance in the human hippocampus at different stages across the lifespan. We identified common molecular hallmarks of human imGCs across the lifespan and observed age-dependent transcriptional dynamics in human imGCs that suggest changes in cellular functionality, niche interactions and disease relevance, that differ from those in mice9. We also found a decreased number of imGCs with altered gene expression in Alzheimer's disease. Finally, we demonstrated the capacity for neurogenesis in the adult human hippocampus with the presence of rare dentate granule cell fate-specific proliferating neural progenitors and with cultured surgical specimens. Together, our findings suggest the presence of a substantial number of imGCs in the adult human hippocampus via low-frequency de novo generation and protracted maturation, and our study reveals their molecular properties across the lifespan and in Alzheimer's disease.

Single-nucleus transcriptomic survey of cell diversity and functional maturation in postnatal mammalian hearts.

A fundamental challenge in understanding cardiac biology and disease is that the remarkable heterogeneity in cell type composition and functional states have not been well characterized at single-cell resolution in maturing and diseased mammalian hearts. Massively parallel single-nucleus RNA sequencing (snRNA-seq) has emerged as a powerful tool to address these questions by interrogating the transcriptome of tens of thousands of nuclei isolated from fresh or frozen tissues. snRNA-seq overcomes the technical challenge of isolating intact single cells from complex tissues, including the maturing mammalian hearts; reduces biased recovery of easily dissociated cell types; and minimizes aberrant gene expression during the whole-cell dissociation. Here we applied sNucDrop-seq, a droplet microfluidics-based massively parallel snRNA-seq method, to investigate the transcriptional landscape of postnatal maturing mouse hearts in both healthy and disease states. By profiling the transcriptome of nearly 20,000 nuclei, we identified major and rare cardiac cell types and revealed significant heterogeneity of cardiomyocytes, fibroblasts, and endothelial cells in postnatal developing hearts. When applied to a mouse model of pediatric mitochondrial cardiomyopathy, we uncovered profound cell type-specific modifications of the cardiac transcriptional landscape at single-nucleus resolution, including changes of subtype composition, maturation states, and functional remodeling of each cell type. Furthermore, we employed sNucDrop-seq to decipher the cardiac cell type-specific gene regulatory network (GRN) of GDF15, a heart-derived hormone and clinically important diagnostic biomarker of heart disease. Together, our results present a rich resource for studying cardiac biology and provide new insights into heart disease using an approach broadly applicable to many fields of biomedicine.

Vanadium Oxide: Phase Diagrams, Structures, Synthesis, and Applications.

Vanadium oxides with multioxidation states and various crystalline structures offer unique electrical, optical, optoelectronic and magnetic properties, which could be manipulated for various applications. For the past 30 years, significant efforts have been made to study the fundamental science and explore the potential for vanadium oxide materials in ion batteries, water splitting, smart windows, supercapacitors, sensors, and so on. This review focuses on the most recent progress in synthesis methods and applications of some thermodynamically stable and metastable vanadium oxides, including but not limited to V2O3, V3O5, VO2, V3O7, V2O5, V2O2, V6O13, and V4O9. We begin with a tutorial on the phase diagram of the V-O system. The second part is a detailed review covering the crystal structure, the synthesis protocols, and the applications of each vanadium oxide, especially in batteries, catalysts, smart windows, and supercapacitors. We conclude with a brief perspective on how material and device improvements can address current deficiencies. This comprehensive review could accelerate the development of novel vanadium oxide structures in related applications.

Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq.

Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues such as adult mammalian brains is challenging. Here, we integrate sucrose-gradient-assisted purification of nuclei with droplet microfluidics to develop a highly scalable single-nucleus RNA-seq approach (sNucDrop-seq), which is free of enzymatic dissociation and nucleus sorting. By profiling ∼18,000 nuclei isolated from cortical tissues of adult mice, we demonstrate that sNucDrop-seq not only accurately reveals neuronal and non-neuronal subtype composition with high sensitivity but also enables in-depth analysis of transient transcriptional states driven by neuronal activity, at single-cell resolution, in vivo.

Darolutamide does not interfere with OATP-mediated uptake of docetaxel.

The addition of darolutamide, an androgen receptor signalling inhibitor, to therapy with docetaxel has recently been approved as a strategy to treat metastatic prostate cancer. OATP1B3 is an SLC transporter that is highly expressed in prostate cancer and is responsible for the accumulation of substrates, including docetaxel, into tumours. Given that darolutamide inhibits OATP1B3 in vitro, we sought to characterise the impact of darolutamide on docetaxel pharmacokinetics. We investigated the influence of darolutamide on OATP1B3 transport using in vitro and in vivo models. We assessed the impact of darolutamide on the tumour accumulation of docetaxel in a patient-derived xenograft (PDX) model and on an OATP1B biomarker in patients. Darolutamide inhibited OATP1B3 in vitro at concentrations higher than the reported Cmax. Consistent with these findings, in vivo studies revealed that darolutamide does not influence the pharmacokinetics of Oatp1b substrates, including docetaxel. Docetaxel accumulation in PDX tumours was not decreased in the presence of darolutamide. Metastatic prostate cancer patients had similar levels of OATP1B biomarkers, regardless of treatment with darolutamide. Consistent with a low potential to inhibit OATP1B3-mediated transport in vitro, darolutamide does not significantly impede the transport of Oatp1b substrates in vivo or in patients. Our findings support combined treatment with docetaxel and darolutamide, as no OATP1B3 transporter based drug-drug interaction was identified.

GW9 determines grain size and floral organ identity in rice.

Grain weight is an important determinant of grain yield. However, the underlying regulatory mechanisms for grain size remain to be fully elucidated. Here, we identify a rice mutant grain weight 9 (gw9), which exhibits larger and heavier grains due to excessive cell proliferation and expansion in spikelet hull. GW9 encodes a nucleus-localized protein containing both C2H2 zinc finger (C2H2-ZnF) and VRN2-EMF2-FIS2-SUZ12 (VEFS) domains, serving as a negative regulator of grain size and weight. Interestingly, the non-frameshift mutations in C2H2-ZnF domain result in increased plant height and larger grain size, whereas frameshift mutations in both C2H2-ZnF and VEFS domains lead to dwarf and malformed spikelet. These observations indicated the dual functions of GW9 in regulating grain size and floral organ identity through the C2H2-ZnF and VEFS domains, respectively. Further investigation revealed the interaction between GW9 and the E3 ubiquitin ligase protein GW2, with GW9 being the target of ubiquitination by GW2. Genetic analyses suggest that GW9 and GW2 function in a coordinated pathway controlling grain size and weight. Our findings provide a novel insight into the functional role of GW9 in the regulation of grain size and weight, offering potential molecular strategies for improving rice yield.

A geometric deep learning framework for drug repositioning over heterogeneous information networks.

Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS: In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping.

MOTIVATION: The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal and fungal populations, while less attention is paid to viral communities. RESULTS: We present KMCP (K-mer-based Metagenomic Classification and Profiling), a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth. AVAILABILITY AND IMPLEMENTATION: The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical characteristics and immunogenicity after Omicron breakthrough infection in patients with chronic hepatitis B infection: A longitudinal observational study.

The clinical and immunological features after breakthrough infection (BTI) during Omicron wave in patients with chronic hepatitis B virus infection (CHB) are still unclear. A total of 101 patients with CHB from our previous coronavirus disease 2019 (COVID-19) vaccination cohort (NCT05007665), were continued to be followed up at the Second Affiliated Hospital of Chongqing Medical University after BTI, while an additional 39 healthcare workers after BTI were recruited as healthy controls (HCs). Clinical data were collected using questionnaire survey and electronic medical record. Blood samples were used to determine the antibody responses, as well as B and T cell responses. After BTI, the clinical symptoms of COVID-19 were mild to moderate in patients with CHB, with a median duration of 5 days. Compared with HCs, patients with CHB were more susceptible to develop moderate COVID-19. The liver function was not significantly damaged, and HBV-DNA was not activated in patients with CHB after BTI. Patients with CHB could elicit robust antibody responses after BTI (NAbs 13.0-fold, BA.5 IgG: 24.2-fold, respectively), which was also significantly higher than that in every period after vaccination (all p < 0.001), and compared to that in HCs after BTI. The CD4+, cTfh, and CD8+ T cell responses were also augmented in patients with CHB after BTI, while exhibiting comparability to those observed in HCs. In patients with CHB after BTI, the immune imprint was observed in B cell responses, rather than in T cell responses. In conclusion, Omicron breakthrough infection induced mild to moderate COVID-19 symptoms in patients with CHB, without exacerbating the progress of liver diseases. Meanwhile, BTI demonstrated the ability to induce robust antibody and T cell responses in patients with CHB, which was comparable to those observed in HCs.

Clinical and immunological features of COVID-19 in patients with anti-MDA5 dermatomyositis during the omicron wave in Chongqing, China.

Patients with anti-melanoma differentiation-associated gene 5 (anti-MDA5) dermatomyositis (DM) have a higher risk of coronavirus disease 2019 (COVID-19) infection. In this longitudinal observational study, we aimed to investigate the clinical and immunological features of these patients after COVID-19 infection. A total of 73 patients with anti-MDA5 DM were recruited from the Second Affiliated Hospital of Chongqing Medical University during the Omicron wave epidemic. Clinical data were collected by questionnaire survey and electronic medical records. Blood samples were used to determine the immunity responses. From December 9, 2022 to March 31, 2023, 67 patients were eligible for final analysis; 68.7% of them were infected with COVID-19. The most common symptoms observed in COVID-19 were upper respiratory symptoms, most cases were mild or moderate (97.8%). The clinical laboratory indexes were relativity stable in patients after infection (all p > 0.05). Vaccination is not a protective factor against the Omicron infection (odds ratio: 2.69, 95% confidence interval: 0.81-8.93, p = 0.105). Both wildtype (WT) neutralizing antibodies titer and BA.5-specific immunoglobulin G titer were significantly enhanced after infection (all p < 0.01), which was as high as healthy controls (HCs). The memory B-cell responses were similar between the patients with anti-MDA5 DM and HCs (p > 0.05). However, both the WT-specific CD8+ T cells and CD4+ T cells were reduced in patients with anti-MDA5 DM (all p < 0.05). In conclusion, patients with anti-MDA5 DM did not deteriorate the COVID-19, in turn, COVID-19 infection did not increase the risk of anti-MDA5 DM exacerbation. The humoral responses were robust but the cellular responses were weakened after COVID-19 infection.

Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping.

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1ρ estimations. In in vivo studies, the estimated T1, T2, and T1ρ values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 ± 53.80 vs. 1228.34 ± 52.91 ms, p > 0.1; T2: 40.70 ± 2.89 vs. 41.19 ± 2.91 ms, p > 0.1; T1ρ: 45.09 ± 4.47 vs. 45.23 ± 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1ρ maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.

ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function.

BACKGROUND: Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion. METHODS: A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Calinski-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function. RESULTS: A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p < 0.001), and the Calinski-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p < 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p < 0.05), whereas the SNR and CNR values were not significantly different (p = 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible. CONCLUSION: ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.

MSUT2 regulates tau spreading via adenosinergic signaling mediated ASAP1 pathway in neurons.

Inclusions comprised of microtubule-associated protein tau (tau) are implicated in a group of neurodegenerative diseases, collectively known as tauopathies, that include Alzheimer's disease (AD). The spreading of misfolded tau "seeds" along neuronal networks is thought to play a crucial role in the progression of tau pathology. Consequently, restricting the release or uptake of tau seeds may inhibit the spread of tau pathology and potentially halt the advancement of the disease. Previous studies have demonstrated that the Mammalian Suppressor of Tauopathy 2 (MSUT2), an RNA binding protein, modulates tau pathogenesis in a transgenic mouse model. In this study, we investigated the impact of MSUT2 on tau pathogenesis using tau seeding models. Our findings indicate that the loss of MSUT2 mitigates human tau seed-induced pathology in neuron cultures and mouse models. In addition, MSUT2 regulates many gene transcripts, including the Adenosine Receptor 1 (A1AR), and we show that down regulation or inhibition of A1AR modulates the activity of the "ArfGAP with SH3 Domain, Ankyrin Repeat, and PH Domain 1 protein" (ASAP1), thereby influencing the internalization of pathogenic tau seeds into neurons resulting in reduction of tau pathology.

Grading Clear Cell Renal Cell Carcinoma Grade Using Diffusion Relaxation Correlated MR Spectroscopic Imaging.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC, and accurate grading is crucial for prognosis and treatment selection. Biopsy is the reference standard for grading, but MRI methods can improve and complement the grading procedure. PURPOSE: Assess the performance of diffusion relaxation correlation spectroscopic imaging (DR-CSI) in grading ccRCC. STUDY TYPE: Prospective. SUBJECTS: 79 patients (age: 58.1 +/- 11.5 years; 55 male) with ccRCC confirmed by histopathology (grade 1, 7; grade 2, 45; grade 3, 18; grade 4, 9) following surgery. FIELD STRENGTH/SEQUENCE: 3.0 T MRI scanner. DR-CSI with a diffusion-weighted echo-planar imaging sequence and T2-mapping with a multi-echo spin echo sequence. ASSESSMENT: DR-CSI results were analyzed for the solid tumor regions of interest using spectrum segmentation with five sub-region volume fraction metrics (VA , VB , VC , VD , and VE ). The regulations for spectrum segmentation were determined based on the D-T2 spectra of distinct macro-components. Tumor size, voxel-wise T2, and apparent diffusion coefficient (ADC) values were obtained. Histopathology assessed tumor grade (G1-G4) for each case. STATISTICAL TESTS: One-way ANOVA or Kruskal-Wallis test, Spearman's correlation (coefficient, rho), multivariable logistic regression analysis, receiver operating characteristic curve analysis, and DeLong's test. Significance criteria: P < 0.05. RESULTS: Significant differences were found in ADC, T2, DR-CSI VB , and VD among the ccRCC grades. Correlations were found for ccRCC grade to tumor size (rho = 0.419), age (rho = 0.253), VB (rho = 0.553) and VD (rho = -0.378). AUC of VB was slightly larger than ADC in distinguishing low-grade (G1-G2) from high-grade (G3-G4) ccRCC (0.801 vs. 0.762, P = 0.406) and G1 from G2 to G4 (0.796 vs. 0.647, P = 0.175), although not significant. Combining VB , VD , and VE had better diagnostic performance than combining ADC and T2 for differentiating G1 from G2-G4 (AUC: 0.814 vs 0.643). DATA CONCLUSION: DR-CSI parameters are correlated with ccRCC grades, and may help to differentiate ccRCC grades. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 2.

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

Gadolinium-enhanced intracranial aneurysm wall imaging and risk of aneurysm growth and rupture: a multicentre longitudinal cohort study.

OBJECTIVES: In patients with an unruptured intracranial aneurysm, gadolinium enhancement of the aneurysm wall is associated with growth and rupture. However, most previous studies did not have a longitudinal design and did not adjust for aneurysm size, which is the main predictor of aneurysm instability and the most important determinant of wall enhancement. We investigated whether aneurysm wall enhancement predicts aneurysm growth and rupture during follow-up and whether the predictive value was independent of aneurysm size. MATERIALS AND METHODS: In this multicentre longitudinal cohort study, individual patient data were obtained from twelve international cohorts. Inclusion criteria were as follows: 18 years or older with ≥ 1 untreated unruptured intracranial aneurysm < 15 mm; gadolinium-enhanced aneurysm wall imaging and MRA at baseline; and MRA or rupture during follow-up. Patients were included between November 2012 and November 2019. We calculated crude hazard ratios with 95%CI of aneurysm wall enhancement for growth (≥ 1 mm increase) or rupture and adjusted for aneurysm size. RESULTS: In 455 patients (mean age (SD), 60 (13) years; 323 (71%) women) with 559 aneurysms, growth or rupture occurred in 13/194 (6.7%) aneurysms with wall enhancement and in 9/365 (2.5%) aneurysms without enhancement (crude hazard ratio 3.1 [95%CI: 1.3-7.4], adjusted hazard ratio 1.4 [95%CI: 0.5-3.7]) with a median follow-up duration of 1.2 years. CONCLUSIONS: Gadolinium enhancement of the aneurysm wall predicts aneurysm growth or rupture during short-term follow-up, but not independent of aneurysm size. CLINICAL RELEVANCE STATEMENT: Gadolinium-enhanced aneurysm wall imaging is not recommended for short-term prediction of growth and rupture, since it appears to have no additional value to conventional predictors. KEY POINTS: • Although aneurysm wall enhancement is associated with aneurysm instability in cross-sectional studies, it remains unknown whether it predicts risk of aneurysm growth or rupture in longitudinal studies. • Gadolinium enhancement of the aneurysm wall predicts aneurysm growth or rupture during short-term follow-up, but not when adjusting for aneurysm size. • While gadolinium-enhanced aneurysm wall imaging is not recommended for short-term prediction of growth and rupture, it may hold potential for aneurysms smaller than 7 mm.

Unraveling the Prominent Existence of Trace Metals in Photocatalysis: Exploring Iron Impurity Effects.

Metal impurities can complicate the identification of active catalyst species in transition metal catalysis and electrocatalysis, potentially leading to misleading findings. This study investigates the influence of metal impurities on photocatalysis. Specifically, the photocatalytic reaction of inert alkanes using chlorides without the use of an external photocatalyst was studied, achieving successful C(sp3)-H functionalization. The observations reveal that Fe and Cu impurities are challenging to avoid in a typical laboratory environment and are prominently present in normal reaction systems, and iron impurities play a dominant role in the aforementioned apparent 'metal-free' reaction. Additionally, iron exhibits significantly higher catalytic activity compared to Cu, Ce, and Ni at low metal concentrations in the photocatalytic C(sp3)-H functionalization using chlorides. Considering the widespread presence of Fe and Cu impurities in typical laboratory environments, this study serves as a reminder of their involvement in reaction processes.

SCMR expert consensus statement for cardiovascular magnetic resonance of patients with a cardiac implantable electronic device.

Cardiovascular magnetic resonance (CMR) is a proven imaging modality for informing diagnosis and prognosis, guiding therapeutic decisions, and risk stratifying surgical intervention. Patients with a cardiac implantable electronic device (CIED) would be expected to derive particular benefit from CMR given high prevalence of cardiomyopathy and arrhythmia. While several guidelines have been published over the last 16 years, it is important to recognize that both the CIED and CMR technologies, as well as our knowledge in MR safety, have evolved rapidly during that period. Given increasing utilization of CIED over the past decades, there is an unmet need to establish a consensus statement that integrates latest evidence concerning MR safety and CIED and CMR technologies. While experienced centers currently perform CMR in CIED patients, broad availability of CMR in this population is lacking, partially due to limited availability of resources for programming devices and appropriate monitoring, but also related to knowledge gaps regarding the risk-benefit ratio of CMR in this growing population. To address the knowledge gaps, this SCMR Expert Consensus Statement integrates consensus guidelines, primary data, and opinions from experts across disparate fields towards the shared goal of informing evidenced-based decision-making regarding the risk-benefit ratio of CMR for patients with CIEDs.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.