SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q 3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Prediction of protein N-terminal acetylation modification sites based on CNN-BiLSTM-attention model

N-terminal acetylation is one of the most common and important post-translational modifications (PTM) of eukaryotic proteins. PTM plays a crucial role in various cellular processes and disease pathogenesis. Thus, the accurate identification of N-terminal acetylation modifications is important to gain insight into cellular processes and other possible functional mechanisms. Although some algorithmic models have been proposed, most have been developed based on traditional machine learning algorithms and small training datasets. Their practical applications are limited. Nevertheless, deep learning algorithmic models are better at handling high-throughput and complex data. In this study, DeepCBA, a model based on the hybrid framework of convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism deep learning, was constructed to detect the N-terminal acetylation sites. The DeepCBA was built as follows: First, a benchmark dataset was generated by selecting low-redundant protein sequences from the Uniport database and further reducing the redundancy of the protein sequences using the CD-HIT tool. Subsequently, based on the skip-gram model in the word2vec algorithm, tripeptide word vector features were generated on the benchmark dataset. Finally, the CNN, BiLSTM, and attention mechanism were combined, and the tripeptide word vector features were fed into the stacked model for multiple rounds of training. The model performed excellently on independent dataset test, with accuracy and area under the curve of 80.51% and 87.36%, respectively. Altogether, DeepCBA achieved superior performance compared with the baseline model, and significantly outperformed most existing predictors. Additionally, our model can be used to identify disease loci and drug targets.

Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation

Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer, and fusion. Yet, each stage presents its unique issues that, collectively, amplify the problem of catastrophic forgetting. To address these issues at each stage, we propose a tri-enhanced distillation framework. (1) Stochastic Knowledge Augmentation reduces redundancy in knowledge, thereby increasing both the diversity and volume of knowledge derived from the old network. (2) Adaptive Knowledge Transfer selectively captures critical information from the old knowledge, facilitating a more accurate knowledge transfer. (3) Global Uncertainty-Guided Fusion introduces a global uncertainty view of the dataset to fuse the old and new knowledge with reduced bias, promoting a more stable knowledge fusion. Our experimental results not only validate the feasibility of our approach, but also demonstrate its superior performance compared to state-of-the-art methods. We suggest that our innovative tri-enhanced distillation framework may establish a robust benchmark for domain continual medical image segmentation.

Gambogic acid exhibits promising anticancer activity by inhibiting the pentose phosphate pathway in lung cancer mouse model

BACKGROUND

The pentose phosphate pathway (PPP) plays a crucial role in the material and energy metabolism in cancer cells. Targeting 6-phosphogluconate dehydrogenase (6PGD), the rate-limiting enzyme in the PPP metabolic process, to inhibit cellular metabolism is an effective anticancer strategy. In our previous study, we have preliminarily demonstrated that gambogic acid (GA) induced cancer cell death by inhibiting 6PGD and suppressing PPP at the cellular level. However, it is unclear whether GA could suppress cancer cell growth by inhibiting PPP pathway in mouse model.

PURPOSE

This study aimed to confirm that GA as a covalent inhibitor of 6PGD protein and to validate that GA suppresses cancer cell growth by inhibiting the PPP pathway in a mouse model.

METHODS

Cell viability was detected by CCK-8 assays as well as flow cytometry. The protein targets of GA were identified using a chemical probe and activity-based protein profiling (ABPP) technology. The target validation was performed by in-gel fluorescence assay, the Cellular Thermal Shift Assay (CETSA). A lung cancer mouse model was constructed to test the anticancer activity of GA. RNA sequencing was performed to analyze the global effect of GA on gene expression.

RESULTS

The chemical probe of GA exhibited high biological activity in vitro. 6PGD was identified as one of the binding proteins of GA by ABPP. Our findings revealed a direct interaction between GA and 6PGD. We also found that the anti-cancer activity of GA depended on reactive oxygen species (ROS), as evidenced by experiments on cells with 6PGD knocked down. More importantly, GA could effectively reduce the production of the two major metabolites of the PPP in lung tissue and inhibit cancer cell growth in the mouse model. Finally, RNA sequencing data suggested that GA treatment significantly regulated apoptosis and hypoxia-related physiological processes.

CONCLUSION

These results demonstrated that GA was a covalent inhibitor of 6PGD protein. GA effectively suppressed cancer cell growth by inhibiting the PPP pathway without causing significant side effects in the mouse model. Our study provides in vivo evidence that elucidates the anticancer mechanism of GA, which involves the inhibition of 6PGD and modulation of cellular metabolic processes.

High-altitude hypoxia exposure inhibits erythrophagocytosis by inducing macrophage ferroptosis in the spleen

High-altitude polycythemia (HAPC) affects individuals living at high altitudes, characterized by increased red blood cells (RBCs) production in response to hypoxic conditions. The exact mechanisms behind HAPC are not fully understood. We utilized a mouse model exposed to hypobaric hypoxia (HH), replicating the environmental conditions experienced at 6000 m above sea level, coupled with in vitro analysis of primary splenic macrophages under 1% O2 to investigate these mechanisms. Our findings indicate that HH significantly boosts erythropoiesis, leading to erythrocytosis and splenic changes, including initial contraction to splenomegaly over 14 days. A notable decrease in red pulp macrophages (RPMs) in the spleen, essential for RBCs processing, was observed, correlating with increased iron release and signs of ferroptosis. Prolonged exposure to hypoxia further exacerbated these effects, mirrored in human peripheral blood mononuclear cells. Single-cell sequencing showed a marked reduction in macrophage populations, affecting the spleen's ability to clear RBCs and contributing to splenomegaly. Our findings suggest splenic ferroptosis contributes to decreased RPMs, affecting erythrophagocytosis and potentially fostering continuous RBCs production in HAPC. These insights could guide the development of targeted therapies for HAPC, emphasizing the importance of splenic macrophages in disease pathology.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

A novel long non-coding RNA LINC00524 facilitates invasion and metastasis through interaction with TDP43 in breast cancer

Breast cancer (BC) remains a significant health concern worldwide, with metastasis being a primary contributor to patient mortality. While advances in understanding the disease's progression continue, the underlying mechanisms, particularly the roles of long non-coding RNAs (lncRNAs), are not fully deciphered. In this study, we examined the influence of the lncRNA LINC00524 on BC invasion and metastasis. Through meticulous analyses of TCGA and GEO data sets, we observed a conspicuous elevation of LINC00524 expression in BC tissues. This increased expression correlated strongly with a poorer prognosis for BC patients. A detailed Gene Ontology analysis suggested that LINC00524 likely exerts its effects through RNA-binding proteins (RBPs) mechanisms. Experimentally, LINC00524 was demonstrated to amplify BC cell migration, invasion and proliferation in vitro. Additionally, in vivo tests showed its potent role in promoting BC cell growth and metastasis. A pivotal discovery was LINC00524's interaction with TDP43, which leads to the stabilization of TDP43 protein expression, an element associated with unfavourable BC outcomes. In essence, our comprehensive study illuminates how LINC00524 accelerates BC invasion and metastasis by binding to TDP43, presenting potential avenues for therapeutic interventions.

CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

Coronary Artery Fistula and Severe Coronary Artery Stenosis: A Case Report and an Insight for Potential Pathogenesis of Coronary Artery Atherosclerosis

Coronary artery fistulae (CAF) are a rare anomaly characterized by abnormal connections between a coronary artery and a cardiac chamber or a great vessel, with most patients remaining asymptomatic. Despite being predisposed to severe complications like heart failure, patients with CAF infrequently experience severe stenosis in the coronary artery. This study delineates a case involving a 46-year-old male presenting with a fistula bridging the right coronary artery (RCA) and right atrium (RA), manifesting a pronounced 99% stenosis at the right extremity of the coronary artery proximal to the fistula. Concurrently, the individual exhibits six conventional risk factors: age over 40, male gender, hypertension, diabetes, smoking, and hypertriglyceridemia. Following pharmaceutical intervention, the patient was discharged and subjected to extended follow-up. This case highlights the dual processes of "accelerating damage" and "retarding renewal" in the progression of atherosclerosis. Factors such as shear stress, smoking, and hypertension are posited to expedite endothelial cell damage, while aging and diabetes may impede the renewal and repair of these cells. Together with the concept of secondary atherosclerotic plaque healing, this case prompts the introduction of a "Double Endothelial Healings" hypothesis, proposing a potential pathogenetic mechanism for coronary artery atherosclerosis.

Vacancies tailoring lattice anharmonicity of Zintl-type thermoelectrics

While phonon anharmonicity affects lattice thermal conductivity intrinsically and is difficult to be modified, controllable lattice defects routinely function only by scattering phonons extrinsically. Here, through a comprehensive study of crystal structure and lattice dynamics of Zintl-type Sr(Cu,Ag,Zn)Sb thermoelectric compounds using neutron scattering techniques and theoretical simulations, we show that the role of vacancies in suppressing lattice thermal conductivity could extend beyond defect scattering. The vacancies in Sr2ZnSb2 significantly enhance lattice anharmonicity, causing a giant softening and broadening of the entire phonon spectrum and, together with defect scattering, leading to a ~ 86% decrease in the maximum lattice thermal conductivity compared to SrCuSb. We show that this huge lattice change arises from charge density reconstruction, which undermines both interlayer and intralayer atomic bonding strength in the hierarchical structure. These microscopic insights demonstrate a promise of artificially tailoring phonon anharmonicity through lattice defect engineering to manipulate lattice thermal conductivity in the design of energy conversion materials.

Research hotspots of polycystic ovary syndrome and hyperandrogenism from 2008 to 2022: bibliometric analysis

BACKGROUND

Polycystic Ovary Syndrome (PCOS) is the most frequent endocrine disorder in female adults, and hyperandrogenism (HA) is the typical endocrine feature of PCOS. This study aims to investigate the trends and hotspots in the study of PCOS and HA.

METHODS

Literature on Web of Science Core Collection (WoSCC) from 2008 to 2022 was retrieved, and bibliometric analysis was conducted using VOSviewer and CiteSpace software.

RESULTS

A total of 2,404 papers were published in 575 journals by 10,121 authors from 2,434 institutions in 86 countries. The number of publications in this field is generally on the rise yearly. The US, China and Italy contributed almost half of the publications. Monash University had the highest number of publications, while the University of Adelaide had the highest average citations and the Karolinska Institute had the strongest cooperation with other institutions. Lergo RS contributed the most to the field of PCOS and HA. The research on PCOS and HA mainly focused on complications, adipose tissue, inflammation, granulosa cells, gene and receptor expression.

CONCLUSION

Different countries, institutions, and authors should facilitate cooperation and exchanges. This study will be helpful for better understanding the frontiers and hotspots in the areas of PCOS and HA.

GSL-DTI: Graph structure learning network for Drug-Target interaction prediction

MOTIVATION

Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins.

RESULTS

We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.

Removal of U(VI) from acidic wastewater by persimmon tannin-functionalized chitosan

With sodium tripolyphosphate (STPP) as cross-linker, Persimmon tannin-chitosan microspheres (PT-CS) were synthesized by hydrothermal for removing U(VI) from acidic effluent. The batch experiments indicated that PT-CS adsorbed U(VI) most effectively at pH 1.5, the maximum adsorption capacity for PT-CS was 245 mg/g. Compared to pure CS dissolved at pH 3, PT-CS still maintain high stability at pH 1. Moreover, single system of common metal ions in rare earth wastewater only slightly affected the adsorption of uranium at pH 1.5, but this process was inhibited about 30% at pH 5. Those results indicated that the selectivity of PT-CS for uranium removal could be controlled by regulating the pH and there are excellent potentials for PT-CS using in acid metal water treatment. Its adsorption selectivity and ability to adapt different condition was demonstrated with uraniferous rare earth wastewater treatment. The adsorption for PT-CS to U(VI) were well fitted for both Langmuir isothern and pseudo-secondary kinetic model equations, and that meant chemisorption dominated the removal process. Spectroscopic analyses confirmed that the adsorption of U(VI) occurred via surface complexation by -OH and ion exchange with Na+. Therefore, this study provides a high-efficiency, low-cost, valuable and highly adaptable method for the treatment of acidic uranium-containing effluents.

Unraveling the complex pathophysiology of white matter hemorrhage in intracerebral stroke: A single-cell RNA sequencing approach

AIM

This study aims to elucidate the cellular dynamics and pathophysiology of white matter hemorrhage (WMH) in intracerebral hemorrhage (ICH).

METHODS

Using varying doses of collagenase IV, a consistent rat ICH model characterized by pronounced WMH was established. Verification was achieved through behavioral assays, hematoma volume, and histological evaluations. Single-cell suspensions from the hemorrhaged region of the ipsilateral striatum on day three post-ICH were profiled using single-cell RNA sequencing (scRNA-seq). Gene Ontology (GO) and gene set variation analysis (GSVA) further interpreted the differentially expressed genes (DEGs).

RESULTS

Following WMH induction, there was a notable increase in the percentage of myeloid cells and oligodendrocyte precursor cells (OPCs), alongside a reduction in the percentage of neurons, microglia, and oligodendrocytes (OLGs). Post-ICH WMH showed homeostatic microglia transitioning into pro-, anti-inflammatory, and proliferative states, influencing lipid metabolic pathways. Myeloid cells amplified chemokine expression, linked with ferroptosis pathways. Macrophages exhibited M1 and M2 phenotypes, and post-WMH, macrophages displayed a predominance of M2 phenotypes, characterized by their anti-inflammatory properties. A surge in OPC proliferation aligned with enhanced ribosomal signaling, suggesting potential reparative responses post-WMH.

CONCLUSION

The study offers valuable insights into WMH's complex pathophysiology following ICH, highlighting the significance and utility of scRNA-seq in understanding the cellular dynamics and contributing to future cerebrovascular research.

Deep medullary vein damage correlates with small vessel disease in small vessel occlusion acute ischemic stroke

OBJECTIVES

We aim to investigate whether cerebral small vessel disease (cSVD) imaging markers correlate with deep medullary vein (DMV) damage in small vessel occlusion acute ischemic stroke (SVO-AIS) patients.

METHODS

The DMV was divided into six segments according to the regional anatomy. The total DMV score (0-18) was calculated based on segmental continuity and visibility. The damage of DMV was grouped according to the quartiles of the total DMV score. Neuroimaging biomarkers of cSVD including white matter hyperintensity (WMH), cerebral microbleed (CMB), perivascular space (PVS), and lacune were identified. The cSVD score were further analyzed.

RESULTS

We included 229 SVO-AIS patients, the mean age was 63.7 ± 23.1 years, the median NIHSS score was 3 (IQR, 2-6). In the severe DMV burden group (the 4th quartile), the NIHSS score grade (6 (3-9)) was significantly higher than other groups (p < 0.01). The grade scores for basal ganglia PVS (BG-PVS) were positively correlated with the degree of DMV (R = 0.67, p < 0.01), rather than centrum semivole PVS (CS-PVS) (R = 0.17, p = 0.1). In multivariate analysis, high CMB burden (adjusted odds ratio [aOR], 25.38; 95% confidence interval [CI], 1.87-345.23) was associated with severe DMV scores. In addition, BG-PVS was related to severe DMV burden in a dose-dependent manner: when BG-PVS score was 3 and 4, the aORs of severe DMV burden were 18.5 and 12.19, respectively.

CONCLUSION

The DMV impairment was associated with the severity of cSVD, which suggests that DMV burden may be used for risk stratification in SVO-AIS patients.

CLINICAL RELEVANCE STATEMENT

The DMV damage score, based on the association between small vessel disease and the deep medullary veins impairment, is a potential new imaging biomarker for the prognosis of small vessel occlusion acute ischemic stroke, with clinical management implications.

KEY POINTS

• The damage to the deep medullary vein may be one mechanism of cerebral small vessel disease. • Severe burden of the basal ganglia perivascular space and cerebral microbleed is closely associated with significant impairment to the deep medullary vein. • The deep medullary vein damage score may reflect a risk of added vascular damage in small vessel occlusion acute ischemic stroke patients.

Effects of biochar layer position on treatment performance and microbial community in subsurface flow constructed wetlands for removal of cadmium and lead

Levels of cadmium (Cd) and lead (Pb) correspond to common composition in acid mine wastewater of Hunan Province of China. The removal path of Cd and Pb and the structure of microbial community were investigated by developing constructed wetlands (CWs) with different layer positions of biochar. The biochar as a layer at the bottom of CW (BCW) system exhibited maximum Cd and Pb removal efficiencies of 96.6-98.6% and 97.2-98.9%, respectively. Compared with original soil, BCW increased the relative proportions of Proteobacteria, Firmicutes, Acidobacteriota, Verrucomicrobiota, Desulfobacterota, Armatimonadota, Bacteroidota, Patescibacteria, Basidiomycota (phylum level) and Burkholderia-Caballeronia-Paraburkholderia, Citrifermentans, Chthonomonadales, Cellulomonas, Geothrix, Terracidiphilus, Gallionellaceae, Microbacterium, Vanrija, Apiotrichum, Saitozyma, Fusarium (genus level). The concentrations of Cd and Pb were positively correlated with the abundance of Verrucomicrobiota, Basidiomycota (phylum level), and Methylacidiphilaceae, Meyerozyma, Vanrija (genus level). This study demonstrates that BCW system can improve removal performance toward Cd and Pb, as well as alter microbial community.

VirGrapher: a graph-based viral identifier for long sequences from metagenomes

Viruses are the most abundant biological entities on earth and are important components of microbial communities. A metagenome contains all microorganisms from an environmental sample. Correctly identifying viruses from these mixed sequences is critical in viral analyses. It is common to identify long viral sequences, which has already been passed thought pipelines of assembly and binning. Existing deep learning-based methods divide these long sequences into short subsequences and identify them separately. This makes the relationships between them be omitted, leading to poor performance on identifying long viral sequences. In this paper, VirGrapher is proposed to improve the identification performance of long viral sequences by constructing relationships among short subsequences from long ones. VirGrapher see a long sequence as a graph and uses a Graph Convolutional Network (GCN) model to learn multilayer connections between nodes from sequences after a GCN-based node embedding model. VirGrapher achieves a better AUC value and accuracy on validation set, which is better than three benchmark methods.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

MRI Assessment of Brain Frailty and Clinical Outcome in Patients With Acute Posterior Perforating Artery Infarction

BACKGROUND

Global brain health has gained increasing attention recently. Imaging markers of brain frailty have been related to functional outcomes in previous studies on anterior circulation; however, little data are available on imaging markers and posterior circulation.

PURPOSE

To investigate the impact of brain frailty on functional outcomes in patients with acute perforating artery infarction (PAI) of the posterior circulation.

STUDY TYPE

Prospective.

POPULATION

One hundred patients (60.78 ± 9.51 years, 72% men) with acute posterior circulation PAI (determined by diffusion-weighted magnetic resonance imaging (MRI)/time-of-flight MR angiography).

FIELD STRENGTH/SEQUENCE

T1- and T2-weighted fast spin echo, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted echo planar, gradient echo (susceptibility-weight imaging), and 3D time-of-flight MR angiography sequences at 3.0 T.

ASSESSMENT

Periventricular and deep white matter hyperintensities (WMH), enlarged perivascular spaces (EPVS) in the basal ganglia and centrum semiovale area, lacunes, cerebral microbleeds (CMB), and total brain frailty score by calculating the above imaging characters were rated visually by three radiologists with 9, 10, and 11 years of experience and one neuroradiologist with 12. Infarction volume was assessed using baseline diffusion-weighted imaging (DWI) data obtained within 24 hours of symptom onset. A modified Rankin Scale (mRS) score >1 on day 90 defined an adverse functional outcome. Associations between the imaging markers of brain frailty and functional outcomes were assessed.

STATISTICAL TESTS

Fisher's exact test, Mann-Whitney U test, and multivariable binary logistic regression. A P value <0.05 was considered statistically significant.

RESULTS

Adverse prognoses (mRS > 1) were observed in 34 (34%) patients. Infarction volume, periventricular WMH, deep WMH, basal ganglia EPVS, CMB, and the brain frailty score were significantly associated with adverse functional outcomes. An increased brain frailty score was significantly associated with unfavorable mRS score on day 90 (odds ratio 1.773, 95% confidence interval 1.237-2.541).

DATA CONCLUSION

Advanced MRI imaging markers of brain frailty, individually or combined as a total brain frailty score, were associated with worse functional outcomes after acute posterior circulation PAI.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 3.

Risk Factors Associated with Cage Retropulsion After Lumbar Interbody Fusion

AIM

To identify the cage retropulsion (CR)-associated risk factors following lumbar interbody fusion (LIF).

MATERIAL AND METHODS

Clinical data of patients who underwent LIF between January 2014 and December 2018 at three medical centers were retrospectively analyzed. Patients were divided into CR group and non-CR (NCR) group according to whether they experienced CR or not. This study analyzed radiological and surgical parameters to identify the risk factors associated with CR.

RESULTS

The enrolled 823 patients who underwent LIF had a total of 1205 disk levels. There were 387 men and 436 women, with a mean age of 58.8 (range, 33-86) years old. The average follow-up time was 16.6 (range, 12-27) months. CR was found in 21 patients (9 men and 12 women, 21 levels). Besides, 14 patients complained of radicular pain postoperatively, of whom 10 patients were recovered after conservative treatment, while the remaining 4 patients further required revision surgery. The mean age was 62.3 ± 8.1 (range, 44-74) years old in the CR group and 59.7 ± 9.7 (range, 33-86) years old in the NCR group. The incidence of CR was higher in patients with osteoporosis than those with a normal bone mineral density (BMD). Moreover, 12 of 21 patients had osteoporosis (57.1%), however, only 29.2% of patients without CR had osteoporosis. The cages of retropulsion were all placed at the posterior disk space by immediately postoperative X-ray or computed tomography (CT) scan. On the contrary, only 35.6% of cages were placed at the posterior disk space in the NCR group. Pear-shaped disk was found in 10 of 21 patients in the CR group (47.6%), whereas it was noted in only 13.4% of cases in the NCR group. Furthermore, 13 out of 21 patients in the CR group experienced intraoperative endplate injury (61.9%), while only 13.4% of patients experienced that in the NCR group. Risk factors for CR were osteoporosis [odds ratio (OR)=8.7, 95% confidence interval (CI) (3.42-34.6), P=0.01], posterior cage position [OR=5.8, 95%CI (2.12-24.6), p=0.03], pear-shaped disk [OR=9.9, 95%CI (6.21-46.42), p < 0.001], and intraoperative endplate injury [OR=9.9, 95%CI (6.21-46.42), p < 0.001].

CONCLUSION

Intraoperative endplate injury, pear-shaped disk, osteoporosis, and posterior cage position were noted as CRassociated risk factors after LIF.

CD4+ T cell immunity is dependent on an intrinsic stem-like program

CD4+ T cells are central to various immune responses, but the molecular programs that drive and maintain CD4+ T cell immunity are not entirely clear. Here we identify a stem-like program that governs the CD4+ T cell response in transplantation models. Single-cell-transcriptomic analysis revealed that naive alloantigen-specific CD4+ T cells develop into TCF1hi effector precursor (TEP) cells and TCF1-CXCR6+ effectors in transplant recipients. The TCF1-CXCR6+CD4+ effectors lose proliferation capacity and do not reject allografts upon adoptive transfer into secondary hosts. By contrast, the TCF1hiCD4+ TEP cells have dual features of self-renewal and effector differentiation potential, and allograft rejection depends on continuous replenishment of TCF1-CXCR6+ effectors from TCF1hiCD4+ TEP cells. Mechanistically, TCF1 sustains the CD4+ TEP cell population, whereas the transcription factor IRF4 and the glycolytic enzyme LDHA govern the effector differentiation potential of CD4+ TEP cells. Deletion of IRF4 or LDHA in T cells induces transplant acceptance. These findings unravel a stem-like program that controls the self-renewal capacity and effector differentiation potential of CD4+ TEP cells and have implications for T cell-related immunotherapies.

MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning

MOTIVATION

Cancer is a complex disease that results in a significant number of global fatalities. Treatment strategies can vary among patients, even if they have the same type of cancer. The application of precision medicine in cancer shows promise for treating different types of cancer, reducing healthcare expenses, and improving recovery rates. To achieve personalized cancer treatment, machine learning models have been developed to predict drug responses based on tumor and drug characteristics. However, current studies either focus on constructing homogeneous networks from single data source or heterogeneous networks from multiomics data. While multiomics data have shown potential in predicting drug responses in cancer cell lines, there is still a lack of research that effectively utilizes insights from different modalities. Furthermore, effectively utilizing the multimodal knowledge of cancer cell lines poses a challenge due to the heterogeneity inherent in these modalities.

RESULTS

To address these challenges, we introduce MMCL-CDR (Multimodal Contrastive Learning for Cancer Drug Responses), a multimodal approach for cancer drug response prediction that integrates copy number variation, gene expression, morphology images of cell lines, and chemical structure of drugs. The objective of MMCL-CDR is to align cancer cell lines across different data modalities by learning cell line representations from omic and image data, and combined with structural drug representations to enhance the prediction of cancer drug responses (CDR). We have carried out comprehensive experiments and show that our model significantly outperforms other state-of-the-art methods in CDR prediction. The experimental results also prove that the model can learn more accurate cell line representation by integrating multiomics and morphological data from cell lines, thereby improving the accuracy of CDR prediction. In addition, the ablation study and qualitative analysis also confirm the effectiveness of each part of our proposed model. Last but not least, MMCL-CDR opens up a new dimension for cancer drug response prediction through multimodal contrastive learning, pioneering a novel approach that integrates multiomics and multimodal drug and cell line modeling.

AVAILABILITY AND IMPLEMENTATION

MMCL-CDR is available at https://github.com/catly/MMCL-CDR.

MDformer: A transformer-based method for predicting miRNA-Disease associations using multi-source feature fusion and maximal meta-path instances encoding

There is a growing body of evidence suggesting that microRNAs (miRNAs), small biological molecules, play a crucial role in the diagnosis, treatment, and prognostic assessment of diseases. However, it is often inefficient to verify the association between miRNAs and diseases (MDA) through traditional experimental methods. Based on this situation, researchers have proposed various computational-based methods, but the existing methods often have many drawbacks in terms of predictive effectiveness and accuracy. Therefore, in order to improve the prediction performance of computational methods, we propose a transformer-based prediction model (MDformer) for multi-source feature information. Specifically, first, we consider multiple features of miRNAs and diseases from the molecular biology perspective and utilize them in a fusion. Then high-quality node feature embeddings were generated using a feature encoder based on the transformer architecture and meta-path instances. Finally, a deep neural network was built for MDA prediction. To evaluate the performance of our model, we performed multiple 5-fold cross-validations as well as comparison experiments on HMDD v3.2 and HMDD v2.0 databases, and the experimental results of the average ROC area under the curve (AUC) were higher than the comparative methods for both databases at 0.9506 and 0.9369. We conducted case studies on five highly lethal cancers (breast, lung, colorectal, gastric, and hepatocellular cancers), and the first 30 predictions for these five diseases achieved 97.3% accuracy. In conclusion, MDformer is a reliable and scientifically sound tool that can be used to accurately predict MDA. In addition, the source code is available at https://github.com/Linda908/MDformer.

Cortical Endplate Bone Density Measured by Novel Phantomless Quantitative Computed Tomography May Predict Cage Subsidence more Conveniently and Accurately

OBJECTIVE

Previous studies have shown that bone mineral density (BMD) is a predictor of cage subsidence. Phantom-less quantitative computed tomography (PL-QCT) can measure volumetric bone mineral density (vBMD) of lumbar trabecular and cortical bone. The study of endplate vBMD (EP-vBMD) is important in predicting cage settlement after extreme lateral interbody fusion (XLIF). This study aimed to determine the risk factors for postoperative cage subsidence after XLIF, particularly focusing on the relationship between vBMD measured by automatic PL-QCT and cage subsidence.

METHODS

Patients who underwent XLIF surgery from January 2018 to October 2020 with a minimum of 6 months of follow-up were retrospectively included. Cage subsidence was defined as >2 mm cage sinking on the adjacent endplate in follow-up imaging evaluation. Outcome measures were localized vBMDs included EP-vBMDs with different region of interest (ROI) heights measured by PL-QCT based on a customized muscle-fat algorithm. Shapiro-Wilk test, one-way ANOVA, Mann-Whitney test, Fisher exact test, univariable and multivariable logistic regression and receiver operating characteristic (ROC) curve analysis were executed in this study.

RESULTS

One hundred and thirteen levels of 78 patients were included in the analysis. The mean age was 65 ± 7.9 years for 11 males and 67 females. Cage subsidence occurred on 45 (39.8%) surgical levels. There was no significant difference in demographics, fused levels, or preoperative radiographic parameters. 1.25-mm EP-vBMD (0.991 [0.985,0.997], p = 0.004) and P-TB-vBMD (cage-positioned trabecular volumetric bone mineral density) (0.988 [0.977-0.999], p = 0.026) were cage-subsidence relevant according to univariate analysis. Low 1.25-mm EP-vBMD (0.992 [0.985, 0.999], p = 0.029) was an independent risk factor according to multifactorial analysis.

CONCLUSION

Preoperative low EP-vBMD was an independent risk factor for postoperative cage subsidence after XLIF. EP-vBMD measured by most cortex-occupied ROI may be the optimal vBMD parameter for cage subsidence prediction.

GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data

With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integrating scRNA-seq and ST data into a unified graph structure, GTAD outperforms traditional 'pseudo-ST' methods, providing robust and information-rich results. GTAD performs exceptionally well with synthesized spatial data and accurately identifies cell spatial composition in tissues like the mouse cerebral cortex, cerebellum, developing human heart and pancreatic ductal carcinoma. GTAD holds the potential to enhance our understanding of tissue microenvironments and cellular diversity in complex bio-logical systems. The source code is available at https://github.com/zzhjs/GTAD.

Hypobaric hypoxia induces iron mobilization from liver and spleen and increases serum iron via activation of ghrelin/GHSR1a/MAPK signalling pathway in mice

Hypobaric hypoxia (HH) exposure affects appetite and serum iron levels in both humans and animals. Thus, whether appetite-regulating ghrelin is involved in iron regulation under HH needs to be elucidated. In vivo, C57BL/6J mice were placed in a hypobaric chamber to establish a 6000-m-high altitude exposure animal model. In vitro, mouse primary hepatocytes and peritoneal macrophages were exposed to hypoxia (1% O2) to examine the effects of ghrelin on iron-regulating proteins. HH obviously reduced the body weight of mice and significantly increased the levels of erythrocytes, and also significantly enhanced the levels of serum iron and plasma ghrelin. However, iron content in the liver and spleen was decreased, while ferroportin (Fpn) expression was increased. Moreover, ghrelin significantly induced Fpn and pERK expression in both hepatocytes and macrophages under hypoxia, which were reversed by pretreatment with growth hormone secretagogue receptor 1a (GHSR1a) antagonist or pERK inhibitor. Our findings indicated that HH leads to decreased appetite and insufficient dietary intake, which may negatively regulate the levels of ghrelin. Furthermore, GHSR1a/ERK signalling pathway is further activated to upregulate the expression of Fpn, and then promoting iron mobilization both in the liver/hepatocytes and spleen/macrophages in mice. Thus, these results revealed that ghrelin may be a potential iron regulatory hormone, and raised the possibility of ghrelin as a promising therapeutic target against iron disorders under HH.

MicroRNA Promoter Identification in Human With a Three-level Prediction Method

The accurate annotation of miRNA promoters is critical for the mechanistic understanding of miRNA gene regulation. Various computational methods have been developed for the prediction of miRNA promoters solely employing a single classifier. Most of these computational methods extract either sequence features or one-sided signal features, and the accuracy and reliability of predictions need to be improved. To address these issues, we present miPTP, a three-level prediction method that combines SVM, RF, and correlation coefficients. It is capable of identifying miRNA promoters based on both DNA sequence and ChIP-Seq data (RPol II). By sequentially integrating these two types of information sources with the three methods selected, miPTP can identify miRNA promoters with higher accuracy and sensitivity compared to specific existing methods. Finally, the reliability of miPTP is validated by examining the conservation, CpG content, and activating histone marks in the identified miRNA promoters.

Bioequivalence of Two 6-Mercaptopurine Tablet Formulations in Healthy Fasting Chinese Volunteers

The supply of branded 6-mercaptopurine (6-MP) is limited in China, necessitating the local production and clinical evaluation of generic alternatives. We evaluated the in vivo bioequivalence (BE) of a new generic mercaptopurine tablet (50 mg) formulation by comparing peak plasma concentration and area under the concentration-time curve (AUC) with a branded 6-MP formulation as the reference in 36 healthy fasting Chinese adults. The in vivo BE was evaluated by the average BE test. The safety parameters of the test and reference formulations were also evaluated. The geometric mean ratios for AUC over the dosing interval and AUC from time zero to infinity were 104% and 104%, respectively, of the reference values, while the point estimate of the geometric mean ratio for peak plasma concentration was 104% of the reference value. The test and reference formulations in this study were both deemed safe as only 23 Grade 1 adverse events were observed in 13 of 36 subjects. The test and reference formulations of 6-MP tablets meet the regulatory criteria for BE in healthy fasting Chinese adults.

Amputation and limb salvage following endovascular and open surgery for the treatment of peripheral artery illnesses: A meta-analysis

A meta-analysis investigation was executed to measure the outcome of endovascular surgery (ES) and open surgery (OS) for the management of peripheral artery diseases (PADs) on amputation and limb salvage (LS). A comprehensive literature inspection till February 2023 was applied and 3451 interrelated investigations were reviewed. The 31 chosen investigations enclosed 19 948 individuals with PADs were in the chosen investigations' starting point, 8861 of them were utilising ES, and 11 087 were utilising OS. Odds ratio (OR) in addition to 95% confidence intervals (CIs) were utilised to compute the value of the effect of ES and OS for the management of PADs on amputation and LS by the dichotomous approaches and a fixed or random model. ES had significantly lower amputation (OR, 0.80; 95% CI, 0.68-0.93, P = 0.005) compared with those with OS in individuals with PADs. No significant difference was found between ES and OS in 30-day LS (OR, 0.95; 95% CI, 0.64-1.42, P = 0.81), 1-year LS (OR, 1.06; 95% CI, 0.81-1.39, P = 0.68), and 3-year LS (OR, 0.86; 95% CI, 0.61-1.19, P = 0.36) in individuals with PADs. ES had significantly lower amputation, 30-day LS, 1-year LS, and 3-year LS compared with those with OS in individuals with PADs. However, care must be exercised when dealing with its values because of the low sample size of some of the nominated investigations for the meta-analysis.

Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics

Objective

This study aimed to explore the radiomics model based on magnetic resonance imaging (MRI) T2WI and compare the value of different machine algorithms in preoperatively predicting tumor budding (TB) grading in rectal cancer.

Methods

A retrospective study was conducted on 266 patients with preoperative rectal MRI examinations, who underwent complete surgical resection and confirmed pathological diagnosis of rectal cancer. Among them, patients from Qingdao West Coast Hospital were assigned as the training group (n=172), while patients from other hospitals were assigned as the external validation group (n=94). Regions of interest (ROIs) were delineated, and image features were extracted and dimensionally reduced using the Least Absolute Shrinkage and Selection Operator (LASSO). Eight machine algorithms were used to construct the models, and the diagnostic performance of the models was evaluated and compared using receiver operating characteristic (ROC) curves and the area under the curve (AUC), as well as clinical utility assessment using decision curve analysis (DCA).

Results

A total of 1197 features were extracted, and after feature selection and dimension reduction, 11 image features related to TB grading were obtained. Among the eight algorithm models, the support vector machine (SVM) algorithm achieved the best diagnostic performance, with accuracy, sensitivity, and specificity of 0.826, 0.949, and 0.723 in the training group, and 0.713, 0.579, and 0.804 in the validation group, respectively. DCA demonstrated the clinical utility of this radiomics model.

Conclusion

The radiomics model based on MR T2WI can provide an effective and noninvasive method for preoperative TB grading assessment in patients with rectal cancer.

DAEMDA: A Method with Dual-Channel Attention Encoding for miRNA-Disease Association Prediction

A growing number of studies have shown that aberrant microRNA (miRNA) expression is closely associated with the evolution and development of various complex human diseases. These key biomarkers' identification and observation are significant for gaining a deeper understanding of disease pathogenesis and therapeutic mechanisms. Consequently, pinpointing potential miRNA-disease associations (MDA) has become a prominent bioinformatics subject, encouraging several new computational methods given the advances in graph neural networks (GNN). Nevertheless, these existing methods commonly fail to exploit the network nodes' global feature information, leaving the generation of high-quality embedding representations using graph properties as a critical unsolved issue. Addressing these challenges, we introduce the DAEMDA, a computational method designed to optimize the current models' efficacy. First, we construct similarity and heterogeneous networks involving miRNAs and diseases, relying on experimentally corroborated miRNA-disease association data and analogous information. Then, a newly-fashioned parallel dual-channel feature encoder, designed to better comprehend the global information within the heterogeneous network and generate varying embedding representations, follows this. Ultimately, employing a neural network classifier, we merge the dual-channel embedding representations and undertake association predictions between miRNA and disease nodes. The experimental results of five-fold cross-validation and case studies of major diseases based on the HMDD v3.2 database show that this method can generate high-quality embedded representations and effectively improve the accuracy of MDA prediction.

Two genetic variants in the SRD5A2 gene are found to be associated with sex differences in the disease characteristics of patients with chronic hepatitis B virus infection

BACKGROUND

To examine the expression characteristics of single nucleotide polymorphisms (SNPs) in the SRD5A2 gene and investigate their potential association with differences in the clinical characteristics between sexes in patients with chronic hepatitis B virus (HBV) infection.

METHODS

A total of 30 loci in six genes primarily involved in the metabolism and signaling of sex hormones/sex hormone receptors, namely AKR1C2, AKR1C3, HSD17B6, SRD5A1, SRD5A2, and ESR1, were genotyped in 1007 patients from eight counties (cities) in Northeastern China with chronic HBV infection and 1040 healthy controls, and their association with viral replication characteristics and the differences in disease severity between sexes was assessed. Western blotting was conducted to determine the hepatic SRD5A2 protein level and its relationship with the inflammatory activity and fibrosis degree in male and female patients.

RESULTS

Two SNP loci in the SRD5A2 gene (rs12470143 and rs7594951) exhibited significant differences in genotype and allele frequencies between sexes, with the proportion of T alleles significantly higher in males than in females. It was found that the incidence and severity of HBV-related liver fibrosis were significantly higher in patients with the T/T genotype in SRD5A2 rs12470143 and rs7594951 than those with the non-T/T genotype. Additionally, serum HBV DNA levels were significantly elevated in T/T patients compared to non-T/T patients. Female patients exhibited significantly lower serum DNA levels compared to male patients. Western blot analysis indicated that greater hepatic SRD5A2 protein levels were associated with higher METAVIR inflammation and fibrosis scores. Furthermore, multivariate analysis showed that the two genetic variants in the SRD5A2 gene (rs12470143 C > T, r7594951 C > T), together with the male sex, age > 50 years old, HBeAg positive status, elevated serum HBsAg load, high serum HBV DNA load, and HBV genotype C, were independent risk factors for HBV-related liver fibrosis.

CONCLUSIONS

This study demonstrated that two genetic variants in the SRD5A2 gene (rs12470143 C > T, r7594951 C > T) are associated with sex differences in the clinical characteristics of patients with chronic HBV infection.

Transcriptomic comparison of two selective retinal cell ablation paradigms in zebrafish reveals shared and cell-specific regenerative responses

Retinal Müller glia (MG) can act as stem-like cells to generate new neurons in both zebrafish and mice. In zebrafish, retinal regeneration is innate and robust, resulting in the replacement of lost neurons and restoration of visual function. In mice, exogenous stimulation of MG is required to reveal a dormant and, to date, limited regenerative capacity. Zebrafish studies have been key in revealing factors that promote regenerative responses in the mammalian eye. Increased understanding of how the regenerative potential of MG is regulated in zebrafish may therefore aid efforts to promote retinal repair therapeutically. Developmental signaling pathways are known to coordinate regeneration following widespread retinal cell loss. In contrast, less is known about how regeneration is regulated in the context of retinal degenerative disease, i.e., following the loss of specific retinal cell types. To address this knowledge gap, we compared transcriptomic responses underlying regeneration following targeted loss of rod photoreceptors or bipolar cells. In total, 2,531 differentially expressed genes (DEGs) were identified, with the majority being paradigm specific, including during early MG activation phases, suggesting the nature of the injury/cell loss informs the regenerative process from initiation onward. For example, early modulation of Notch signaling was implicated in the rod but not bipolar cell ablation paradigm and components of JAK/STAT signaling were implicated in both paradigms. To examine candidate gene roles in rod cell regeneration, including several immune-related factors, CRISPR/Cas9 was used to create G0 mutant larvae (i.e., "crispants"). Rod cell regeneration was inhibited in stat3 crispants, while mutating stat5a/b, c7b and txn accelerated rod regeneration kinetics. These data support emerging evidence that discrete responses follow from selective retinal cell loss and that the immune system plays a key role in regulating "fate-biased" regenerative processes.

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome

Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively rely on DNA sequence information for silencer identification fail to account for the cell specificity of silencers, resulting in diminished accuracy. Despite the discovery of several transcription factors and epigenetic modifications associated with silencers on the genome, there is still no definitive biological signal or combination thereof to fully characterize silencers, posing challenges in selecting suitable biological signals for their identification. Therefore, we propose a sophisticated deep learning framework called DeepICSH, which is based on multiple biological data sources. Specifically, DeepICSH leverages a deep convolutional neural network to automatically capture biologically relevant signal combinations strongly associated with silencers, originating from a diverse array of biological signals. Furthermore, the utilization of attention mechanisms facilitates the scoring and visualization of these signal combinations, whereas the employment of skip connections facilitates the fusion of multilevel sequence features and signal combinations, thereby empowering the accurate identification of silencers within specific cells. Extensive experiments on HepG2 and K562 cell line data sets demonstrate that DeepICSH outperforms state-of-the-art methods in silencer identification. Notably, we introduce for the first time a deep learning framework based on multi-omics data for classifying strong and weak silencers, achieving favorable performance. In conclusion, DeepICSH shows great promise for advancing the study and analysis of silencers in complex diseases. The source code is available at https://github.com/lyli1013/DeepICSH.

MTGDC: A Multi-Scale Tensor Graph Diffusion Clustering for Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is a new technology that focuses on the expression levels for each cell to study cell heterogeneity. Thus, new computational methods matching scRNA-seq are designed to detect cell types among various cell groups. Herein, we propose a Multi-scale Tensor Graph Diffusion Clustering (MTGDC) for single-cell RNA sequencing data. It has the following mechanisms: 1) To mine potential similarity distributions among cells, we design a multi-scale affinity learning method to construct a fully connected graph between cells; 2) For each affinity matrix, we propose an efficient tensor graph diffusion learning framework to learn high-order information among multi-scale affinity matrices. First, the tensor graph is explicitly introduced to measure cell-cell edges with local high-order relationship information. To further preserve more global topology structure information in the tensor graph, MTGDC implicitly considers the propagation of information via a data diffusion process by designing a simple and efficient tensor graph diffusion update algorithm. 3) Finally, we mix together the multi-scale tensor graphs to obtain the fusion high-order affinity matrix and apply it to spectral clustering. Experiments and case studies showed that MTGDC had obvious advantages over the state-of-art algorithms in robustness, accuracy, visualization, and speed.

Initiator-Free Photocuring 3D-Printable PVA-Based Hydrogel with Tunable Mechanical Properties and Cell Compatibility

Poly(vinyl alcohol) (PVA)-based hydrogels have attracted great attention and been widely used in biological tissue engineering. With the development of modern medicine, precision medicine requires the customization of medical materials. However, lacking of photocurable functional groups or the performance of rapid phase transition makes PVA-based hydrogels difficult to be customizable molded through photocuring 3D printing technique. In this research, customizable PVA-based hydrogels with high performance through 3D photocurable printing and freezing-thawing (F-T) process are obtained. The ability of 3D-printable is endowed by the introduction of polyvinyl alcohol-styrylpyridine (PVA-SBQ), which can be photo-crosslinked quickly without photoinitiator. Meanwhile, the tunable mechanical properties are achieved by adjusting the mass ratio of PVA-SBQ to PVA, and PVA can offer the physical crosslinking points through freezing-thawing (F-T) process. The hydrogels with high resolution are prepared by digital light procession 3D printing with the mass ratio 1:1 of PVA-SBQ to PVA solution. Attributed to the absence of initiator, and no small molecule residues inside the hydrogels, the hydrogels have good biocompatibility and have the potential to be applicated in the field of biological tissue engineering.

Scientific Knowledge Graph of Dysmenorrhea: A Bibliometric Analysis from 2001 to 2021

Purpose

This study aims to help researchers master the most active hotspots and trends quickly through bibliometric analysis in the field of dysmenorrhea.

Methods

We retrieved literature on Web of Science from 2001 to 2021, and bibliometric analysis software CiteSpace was used in combination with VOSviewer.

Results

We finally acquired 944 papers and an upward trend in articles continued in this field overall. Through the map, China contributed the most, followed by the USA and Turkey. For institutions, Beijing University of Chinese Medicine in China contributed the most, followed by National Yang-Ming University in Taiwan, China. Hsieh JC and Hellman KM were both the most prolific authors with 14 articles. Five major research groups, respectively, with Hsieh JC, Hellman KM, Zhu J, Liang F and Dun W were the key group. Dawood MY was the most dominant author and most frequently cited author. The Cochrane Database of Systematic Reviews Journal was the most productive, and the Fertility and Sterility Journal was the most cited. Advances in pathogenesis and management for primary dysmenorrhea written by Dawood MY was most cited and influential. Pathophysiology, the potential central mechanism, syndrome, evaluation index, diagnosis of adenomyosis-associated dysmenorrhea, treatment, etc., were the main trends and hotspots.

Conclusion

Dysmenorrhea research has received a lot of attention from scholars. Strengthening international cooperation may promote the development of this field. The pathophysiology of dysmenorrhea, its impact on public health and its treatment are current research hotspots and are likely to be the focus of future study.

Preoperative Levosimendan Administration in Heart Transplant Patients with Severe Hepatic and Renal Impairment: A Retrospective Study

BACKGROUND

The cardio-renal syndrome and hepatic impairment play a critical role in end-stage heart failure (HF). Levosimendan is an effective inotropic agent used to maintain cardiac output similar to classic cardiotonic like dobutamine/dopamine. This current research aims to investigate the clinical outcomes of levosimendan and dobutamine/dopamine in Chinese heart transplant awaiting patients with severe hepatic or renal impairment.

METHODS

We performed a retrospective analysis of 568 heart transplant awaiting individuals with severe hepatic or renal impairment who treated with levosimendan or dobutamine/dopamine in our institution between January 2015 and December 2020. Univariate Cox proportional hazard models and Kaplan-Meier survival curves were applied. The primary endpoint was defined as death included inhospital mortality and the mortality at 30 days, 90 days, 180 days and 1 year after heart transplantation.

RESULTS

There were no significant differences in mortality rate at 30, 90, 180 days and 1 years after heart transplantation between the levosimendan and non-levosimendan groups, or between subgroups of patients with severe hepatic impairment or renal impairment. The results were consistent before and after propensity score matching.

CONCLUSIONS

In the population with advanced heart failure awaiting heart transplantation, levosimendan did not increase short- or long-term mortality rates after surgery compared to dobutamine/dopamine, regardless of their hepatic or renal function. Severe hepatic or renal impairment were not necessarily considered a contraindication for levosimendan in these patients.

Association of fasting blood glucose variability with all-cause mortality in heart transplant recipients

BACKGROUND

Fasting blood glucose (FBG) variability, an emerging marker of glycemic control, has been shown to be related to the risk of cardiovascular events and all-cause mortality in subjects with or without diabetes. However, whether FBG variability is independently associated with a higher all-cause mortality in heart transplant recipients remains unknown.

METHODS

We performed a retrospective cohort study including 373 adult recipients who survived for at least 1 year after heart transplantation with a functioning graft and measured FBG more than three times within first year after transplantation. Multivariable adjusted Cox regression analyses were performed to assess the association between FBG variability and all-cause mortality.

RESULTS

Patients were categorized into three groups according to the coefficient of variation of FBG level: ≤7.0%, 7.0%-13.5%, and >13.5%. During a median follow-up of 44.4 months (interquartile range [IQR], 22.6-63.3 months), 31 (8.3%) participants died. In univariate analyses, FBG variability was associated with an increased all-cause mortality (hazard ratio [HR]: 3.00, 95% confidence interval [CI]: 1.67, 5.38; p < .001). This association remained materially unchanged in the multivariable model adjusted for components of demographics, cardiovascular history and lifestyle, hospital information, immunosuppressive therapy, and post-transplant renal function (HR: 2.75, 95% CI: 1.43, 5.28; p = .004).

CONCLUSIONS

After heart transplantation, high FBG variability is strongly and independently associated with an increased risk of all-cause mortality. Our findings suggest that FBG variability is a novel risk factor and prognostic marker for heart transplantation recipients in outpatient clinic.

Extreme phonon anharmonicity underpins superionic diffusion and ultralow thermal conductivity in argyrodite Ag8SnSe6

Ultralow thermal conductivity and fast ionic diffusion endow superionic materials with excellent performance both as thermoelectric converters and as solid-state electrolytes. Yet the correlation and interdependence between these two features remain unclear owing to a limited understanding of their complex atomic dynamics. Here we investigate ionic diffusion and lattice dynamics in argyrodite Ag8SnSe6 using synchrotron X-ray and neutron scattering techniques along with machine-learned molecular dynamics. We identify a critical interplay of the vibrational dynamics of mobile Ag and a host framework that controls the overdamping of low-energy Ag-dominated phonons into a quasi-elastic response, enabling superionicity. Concomitantly, the persistence of long-wavelength transverse acoustic phonons across the superionic transition challenges a proposed 'liquid-like thermal conduction' picture. Rather, a striking thermal broadening of low-energy phonons, starting even below 50 K, reveals extreme phonon anharmonicity and weak bonding as underlying features of the potential energy surface responsible for the ultralow thermal conductivity (<0.5 W m-1 K-1) and fast diffusion. Our results provide fundamental insights into the complex atomic dynamics in superionic materials for energy conversion and storage.

Effects of polyunsaturated fatty acids serum levels on vascular dementia: A two-sample Mendelian randomization study

INTRODUCTION

Several observational studies have indicated that polyunsaturated fatty acids serum levels (PUFAs) are associated with vascular dementia (VaD), but their causal relationships remain elusive. Therefore, we attempted to evaluate the causal effect of PUFAs on VaD in a two-sample Mendelian randomization (MR) analysis by using summary statistics from aggregated genome-wide association studies.

METHODS

The inverse-variance weighted (IVW) method was performed as the primary analysis. Sensitivity analyses (MR-Egger regression, weighted median, penalized weighted median and MR pleiotropy residual sum and outlier methods) were also implemented to estimate the effects of potential violations of MR hypotheses.

RESULTS

No causality was found for PUFAs (OR, 1.14; 95% CI, .91-1.42; p = .25) on VaD in the IVW model. The results were consistent in sensitivity analyses. There was no notable horizontal pleiotropy or heterogeneity.

CONCLUSION

In this two-sample MR analysis, our findings did not support the assumption that PUFAs play causal role in the occurrence or development of VaD.

Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where "disease phenotype" refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level.

RESULTS

PACSI takes advantage of the topological properties of biological networks to introduce a proximity-based measure that quantifies the correlation between each cell and the disease phenotype of interest. Applied to simulated data and four case studies, PACSI accurately identified cells associated with disease phenotypes such as diagnosis, prognosis, and response to immunotherapy. In addition, we demonstrated that PACSI can also be applied to spatial transcriptomics data and successfully label spots that are associated with poor survival of breast carcinoma.

CONCLUSIONS

PACSI is an efficient method to identify cell subpopulations associated with disease phenotypes. Our research shows that it has a broad range of applications in revealing mechanistic and clinical insights of diseases.

Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies

Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mechanisms in MFS via a series bioinformatics and machine learning analysis. First, we downloaded two MFS datasets from the GEO database. A total of 215 differentially expressed genes (DEGs) and 78 differentially expressed miRNAs (DEMs) were identified via "Limma" package. 60 DEGs, mainly enriched in abnormal transportation of structure and energy substances, were selected after protein-protein interaction (PPI) network construction, of which 20 were chosen for machine learning after three algorithms (betweenness, closeness, and degree) filtration using Cytoscape. Four overlapping DEGs (ACTN1, CFTR, GCKR, LAMA3) were finally selected as the candidate markers based on three machine-learning approaches (Lasso, random forest, and support vector machine-recursive feature elimination). Furthermore, we collected peripheral blood from MFS patients and healthy control to validate the findings and the results showed that compared with the control, the expression of the four DEGs was all statistically different in MFS patients validated by qRT-PCR. Besides, the area under the receiver operating characteristics curve was greater than 0.8 for each DEG. Single-sample gene-set enrichment analysis showed that the four DEGs were strongly associated with inflammation and myogenesis pathway. Finally, we constructed the mRNA-miRNA network based on the intersection of DEMs and predicted miRNAs targeting DEGs. In conclusion, our study partially provided four potential markers for MFS pathogenesis.Communicated by Ramaswamy H. Sarma.

DeepITEH: A deep learning framework for identifying tissue-specific eRNAs from the human genome

MOTIVATION

Enhancers are vital cis-regulatory elements that regulate gene expression. eRNAs, a type of lncRNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in the regulation of gene expression and cancer development. Methods that identify eRNAs based solely on genomic sequence data have high error rates because they do not account for tissue specificity. Specific histone modifications associated with eRNAs offer valuable information for their identification. However, identification of eRNAs using histone modification data requires the use of both RNA-seq and histone modification data. Unfortunately, many public datasets contain only one of these components, which impedes the accurate identification of eRNAs.

RESULTS

We introduce DeepITEH, a deep learning framework that leverages RNA-seq data and histone modification data from multiple samples of the same tissue to enhance the accuracy of identifying eRNAs. Specifically, deepITEH initially categorizes eRNAs into two classes, namely, regularly expressed eRNAs (RE) and accidental eRNAs (AE), using histone modification data from multiple samples of the same tissue. Thereafter, it integrates both sequence and histone modification features to identify eRNAs in specific tissues. To evaluate DeepITEH's performance, we compared it with four existing state-of-the-art enhancer prediction methods, SeqPose, iEnhancer-RD, LSTMAtt, and FRL, on four normal tissues and four cancer tissues. Remarkably, seven of these tissues demonstrated a substantially improved specific eRNA prediction performance with DeepITEH, as compared to other methods. Our findings suggest that DeepITEH can effectively predict potential eRNAs on the human genome, providing insights for studying the eRNA function in cancer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Visible-Light-Activated Nickel Thiolates for C-S Couplings

Thiolates are known as the inhibitors of metal catalysis due to their strong coordination with the metal. Herein, we reported visible-light-induced homolysis of the Ni-S bond to activate the nickel(II) thiolates for the C-S coupling, obviating the use of exogenous photocatalysts and other additives. Various aryl bromides/iodides can efficiently couple with thiols with a wide range of functional groups under mild conditions. Preliminary mechanistic studies suggested the homolysis of the Ni-S bond is the key step for couplings and nickel(0) is not involved in the process.

Assessment of patients' preferences for new anticancer drugs in China: a best-worst discrete choice experiment on three common cancer types

OBJECTIVES

Despite the advancement in anticancer drug therapies, cancer treatment decisions are often complex and preference-sensitive, making them well suited for studying shared decision-making (SDM). Our study aimed to assess preferences for new anticancer drugs among three common types of patients with cancer to inform SDM.

DESIGN

We identified five attributes of new anticancer drugs and used a Bayesian-efficient design to generate choice sets for a best-worst discrete choice experiment (BWDCE). The mixed logit regression model was applied to estimate patient-reported preferences for each attribute. The interaction model was used to investigate preference heterogeneity.

SETTING

The BWDCE was conducted in Jiangsu province and Hebei province in China.

PARTICIPANTS

Patients aged 18 years or older, who had a definite diagnosis of lung cancer, breast cancer or colorectal cancer were recruited.

RESULTS

Data from 468 patients were available for analysis. On average, the most valued attribute was the improvement in health-related quality of life (HRQoL) (p<0.001). The low incidence of severe to life-threatening side effects, prolonged progression-free survival and the low incidence of mild to moderate side effects were also positive predictors of patients' preferences (p<0.001). Out-of-pocket cost was a negative predictor of their preferences (p<0.001). According to subgroup analysis by type of cancer, the improvement in HRQoL remained the most valuable attribute. However, the relative importance of other attributes varied by type of cancer. Whether patients were newly diagnosed or previously diagnosed cancer cases played a dominant role in the preference heterogeneity within each subgroup.

CONCLUSIONS

Our study can assist in the implementation of SDM by providing evidence on patients' preferences for new anticancer drugs. Patients should be informed of the multiattribute values of new drugs and encouraged to make decisions reflecting their values.

Microglia-derived TNF-α contributes to RVLM neuronal mitochondrial dysfunction via blocking the AMPK-Sirt3 pathway in stress-induced hypertension

BACKGROUND

Neuroinflammation in the rostral ventrolateral medulla (RVLM) has been associated with the pathogenesis of stress-induced hypertension (SIH). Neuronal mitochondrial dysfunction is involved in many pathological and physiological processes. However, the impact of neuroinflammation on neuronal mitochondrial homeostasis and the involved signaling pathway in the RVLM during SIH are largely unknown.

METHODS

The morphology and phenotype of microglia and the neuronal mitochondrial injury in vivo were analyzed by immunofluorescence, Western blot, RT-qPCR, transmission electron microscopy, and kit detection. The underlying mechanisms of microglia-derived tumor necrosis factor-α (TNF-α) on neuronal mitochondrial function were investigated through in vitro and in vivo experiments such as immunofluorescence and Western blot. The effect of TNF-α on blood pressure (BP) regulation was determined in vivo via intra-RVLM microinjection of TNF-α receptor antagonist R7050.

RESULTS

The results demonstrated that BP, heart rate (HR), renal sympathetic nerve activity (RSNA), plasma norepinephrine (NE), and electroencephalogram (EEG) power increased in SIH rats. Furthermore, the branching complexity of microglia in the RVLM of SIH rats decreased and polarized into M1 phenotype, accompanied by upregulation of TNF-α. Increased neuronal mitochondria injury was observed in the RVLM of SIH rats. Mechanistically, Sirtuin 3 (Sirt3) and p-AMPK expression were markedly downregulated in both SIH rats and TNF-α-treated N2a cells. AMPK activator A769662 upregulated AMPK-Sirt3 signaling pathway and consequently reversed TNF-α-induced mitochondrial dysfunction. Microinjection of TNF-α receptor antagonist R7050 into the RVLM of SIH rats significantly inhibited the biological activities of TNF-α, increased p-AMPK and Sirt3 levels, and alleviated neuronal mitochondrial injury, thereby reducing c-FOS expression, RSNA, plasma NE, and BP.

CONCLUSIONS

This study revealed that microglia-derived TNF-α in the RVLM impairs neuronal mitochondrial function in SIH possibly through inhibiting the AMPK-Sirt3 pathway. Therefore, microglia-derived TNF-α in the RVLM may be a possible therapeutic target for the intervention of SIH.

Dynamic changes of enhancer and super enhancer landscape in degenerated nucleus pulposus cells

Inflammatory cascade and extracellular matrix remodeling have been identified as pivotal pathological factors in the progression of intervertebral disc degeneration (IDD), but the mechanisms underlying the aberrant activation of transcription during nucleus pulposus (NP) cell degeneration remain elusive. Super-enhancers (SEs) are large clusters of adjacent lone enhancers, which control expression modes of cellular fate and pathogenic genes. Here, we showed that SEs underwent tremendous remodeling during NP cell degeneration and that SE-related transcripts were most abundant in inflammatory cascade and extracellular matrix remodeling processes. Inhibition of cyclin-dependent kinase 7, a transcriptional kinase-mediated transcriptional initiation in trans-acting SE complex, constricted the transcription of inflammatory cascades, and extracellular matrix remodeling-related genes such as IL1β and MMP3 in NP cells, meanwhile, also restrained the transcription of Mmp16, Tnfrsf21, and Il11ra1 to retard IDD in rats. In summary, our findings clarify SEs control the transcription of genes associated with inflammatory cascade and extracellular matrix remodeling during NP cell degeneration and identify inhibition of the cyclin-dependent kinase 7, required for SE-mediated transcriptional activation, as a therapeutic option for IDD.

Preparation and Application of pH-Sensitive Film Containing Anthocyanins Extracted from Lycium ruthenicum Murr

A new pH-sensitive film was developed using Artemisia sphaerocephala Krasch. gum (ASKG), soybean protein isolate (SPI), and natural anthocyanin extracted from Lycium ruthenicum Murr. The film was prepared by adsorbing anthocyanins dissolved in an acidified alcohol solution on a solid matrix. ASKG and SPI were used as the solid matrix for the immobilization of the Lycium ruthenicum Murr. anthocyanin extract, which was absorbed into the film as a natural dye using the facile-dip method. Regarding the mechanical properties of the pH-sensitive film, the tensile strength (TS) values increased approximately 2-5-fold, but the elongation at break (EB) values decreased significantly by about 60% to 95%. With the increase in anthocyanin concentration, the oxygen permeability (OP) values first decreased by about 85%, and then increased by about 364%. The water vapor permeability (WVP) values increased by about 63%, and then decreased by about 20%. Colorimetric analysis of the films revealed variations in color at different pH values (pH 2.0-10.0). Fourier-transform infrared (FT-IR) spectra and XRD patterns indicated compatibility among ASKG, SPI, and anthocyanin extracts. In addition, an application test was conducted to establish a correlation between film color change and carp meat spoilage. At storage temperatures of 25 °C and 4 °C, when the meat was totally spoiled, the TVB-N values reached 99.80 ± 2.53 mg/100 g and 58.75 ± 1.49 mg/100 g, and the film's color changed from red to light brown and from red to yellowish green, respectively. Therefore, this pH-sensitive film could be used as an indicator to monitor the freshness of meat during storage.

[Influence of sleep fragmentation in infancy and toddler period on emotional and behavioral problem at the age of 6 years: a birth cohort study]

Objective: To investigate the influence of sleep fragmentation in infancy and toddler period on emotional and behavioral problems at the age of 6 years. Methods: Using a prospective cohort design, 262 children were extracted from mother-child birth cohort recruited from May 2012 to July 2013 in Renji Hospital, School of Medicine, Shanghai Jiao Tong University. Children's sleep and physical activities were assessed using actigraphy at 6, 12, 18, 24, and 36 months of age, from which the sleep fragmentation index (FI) at each follow-up point was calculated. Children's emotional and behavioral problems at 6 years of age were assessed using the strengths and difficulties questionnaire. Group-based trajectory model was applied to determine sleep FI in infancy and toddler period trajectory groups with Bayesian information criteria being used to determine the best fitting model. Children's emotional and behavioral problems between groups were examined with independent t test and linear regression models, etc. Results: A total of 177 children, with 91 boys and 86 girls, were included in the final analysis and were divided into 2 groups: high FI group (n=30) and low FI group (n=147). Compared with children in the low FI group, those in the high FI group presents with higher total difficulties score and higher hyperactivity or inattention score ((11.0±4.9) vs. (8.9±4.1), (4.9±2.7) vs. (3.7±2.3) scores, t=2.17, 2.23, both P<0.05, respectively), with the differences remaining significant after adjusting for covariates (t=2.08, 2.09, both P<0.05 respectively). Conclusion: High sleep fragmentation in infancy and toddler period is associated with more emotional and behavioral problems, especially hyperactivity or inattention problems, at 6 years of age.