scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering.

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback-Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods.

Continuous and Controllable Preparation of Sodium Alginate Hydrogel Tubes Guided by the Soft Cap Inspired by the Apical Growth of the Plant.

Hydrogel tubes made of sodium alginate (SA) have potential applications in drug delivery, soft robots, biomimetic blood vessels, tissue stents, and other fields. However, the continuous preparation of hollow SA hydrogel tubes with good stability and size control remains a huge challenge for chemists, material scientists, and medical practitioners. Inspired by the plant apical growth strategy, a new method named soft cap-guided growth was proposed to produce SA hydrogel tubes. Due to the introduction of inert low gravity substances, such as air and heptane, into the extrusion needle in front of calcium chloride solution to form a soft cap, the SA hydrogel tubes with controllable sizes were fabricated rapidly and continuously without using a template through a negative gravitropism mechanism. The SA hydrogel tubes had good tensile strength, high burst pressure, and good cell compatibility. In addition, hydrogel tubes with complex patterns were conveniently created by controlling the motion path of a soft cap, such as a rotating SA bath or magnetic force. Our research provided a simple innovative technique to steer the growth of hydrogel tubes, which made it possible to mass produce hydrogel tubes with controllable sizes and programmable patterns.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Hierarchical multimodal self-attention-based graph neural network for DTI prediction.

Drug-target interactions (DTIs) are a key part of drug development process and their accurate and efficient prediction can significantly boost development efficiency and reduce development time. Recent years have witnessed the rapid advancement of deep learning, resulting in an abundance of deep learning-based models for DTI prediction. However, most of these models used a single representation of drugs and proteins, making it difficult to comprehensively represent their characteristics. Multimodal data fusion can effectively compensate for the limitations of single-modal data. However, existing multimodal models for DTI prediction do not take into account both intra- and inter-modal interactions simultaneously, resulting in limited presentation capabilities of fused features and a reduction in DTI prediction accuracy. A hierarchical multimodal self-attention-based graph neural network for DTI prediction, called HMSA-DTI, is proposed to address multimodal feature fusion. Our proposed HMSA-DTI takes drug SMILES, drug molecular graphs, protein sequences and protein 2-mer sequences as inputs, and utilizes a hierarchical multimodal self-attention mechanism to achieve deep fusion of multimodal features of drugs and proteins, enabling the capture of intra- and inter-modal interactions between drugs and proteins. It is demonstrated that our proposed HMSA-DTI has significant advantages over other baseline methods on multiple evaluation metrics across five benchmark datasets.

Evaluation of dose delivery based on deformed CT using a commercial software for lung cancer.

This study employed a commercial software velocity to perform deformable registration and dose calculation on deformed CT images, aiming to assess the accuracy of dose delivery during the radiotherapy for lung cancers. A total of 20 patients with lung cancer were enrolled in this study. Adaptive CT (ACT) was generated by deformed the planning CT (pCT) to the CBCT of initial radiotherapy fraction, followed by contour propagation and dose recalculation. There was not significant difference between volumes of GTV and CTV calculated from the ACT and pCT. However, significant differences in dice similarity coefficient (DSC) and coverage ratio (CR) between GTV and CTV were observed, with lower values for GTV volumes below 15 cc. The mean differences in dose corresponding to 95% of the GTV, GTV-P, CTV, and CTV-P between ACT and pCT were - 0.32%, 4.52%, 2.17%, and 4.71%, respectively. For the dose corresponding to 99%, the discrepancies were - 0.18%, 8.35%, 1.92%, and 24.96%, respectively. These differences in dose primarily appeared at the edges of the target areas. Notably, a significant enhancement of dose corresponding to 1 cc for spinal cord was observed in ACT, compared with pCT. There was no statistical difference in the mean dose of lungs and heart. In general, for lung cancer patients, anatomical motion may result in both CTV and GTV moving outside the original irradiation region. The dose difference within the original target area was small, but the difference in the planning target area was considerable.

Impact of the Alberta Stroke Program CT Score subregions on long-term functional outcomes in acute ischemic stroke: Results from two multicenter studies in China.

Background and Objectives: The Alberta Stroke Program CT Score (ASPECTS) is a widely used rating system for assessing infarct extent and location. We aimed to investigate the prognostic value of ASPECTS subregions' involvement in the long-term functional outcomes of acute ischemic stroke (AIS). Materials and Methods: Consecutive patients with AIS and anterior circulation large-vessel stenosis and occlusion between January 2019 and December 2020 were included. The ASPECTS score and subregion involvement for each patient was assessed using posttreatment magnetic resonance diffusion-weighted imaging. Univariate and multivariable regression analyses were conducted to identify subregions related to 3-month poor functional outcome (modified Rankin Scale scores, 3-6) in the reperfusion and medical therapy cohorts, respectively. In addition, prognostic efficiency between the region-based ASPECTS and ASPECTS score methods were compared using receiver operating characteristic curves and DeLong's test. Results: A total of 365 patients (median age, 64 years; 70% men) were included, of whom 169 had poor outcomes. In the reperfusion therapy cohort, multivariable regression analyses revealed that the involvement of the left M4 cortical region in left-hemisphere stroke (adjusted odds ratio [aOR] 5.39, 95% confidence interval [CI] 1.53-19.02) and the involvement of the right M3 cortical region in right-hemisphere stroke (aOR 4.21, 95% CI 1.05-16.78) were independently associated with poor functional outcomes. In the medical therapy cohort, left-hemisphere stroke with left M5 cortical region (aOR 2.87, 95% CI 1.08-7.59) and caudate nucleus (aOR 3.14, 95% CI 1.00-9.85) involved and right-hemisphere stroke with right M3 cortical region (aOR 4.15, 95% CI 1.29-8.18) and internal capsule (aOR 3.94, 95% CI 1.22-12.78) affected were related to the increased risks of poststroke disability. In addition, region-based ASPECTS significantly improved the prognostic efficiency compared with the conventional ASPECTS score method. Conclusion: The involvement of specific ASPECTS subregions depending on the affected hemisphere was associated with worse functional outcomes 3 months after stroke, and the critical subregion distribution varied by clinical management. Therefore, region-based ASPECTS could provide additional value in guiding individual decision making and neurological recovery in patients with AIS.

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.

An analog circuit fault diagnosis method using improved sparrow search algorithm and support vector machine.

In analog circuits, component tolerances and circuit nonlinearity pose obstacles to fault diagnosis. To solve this problem, a soft fault diagnosis method based on Sparrow Search Algorithm (SSA) and Support Vector Machine (SVM) is used. In this study, ISSA is obtained by optimization using four strategies for SSA deficiency. Twenty-three benchmark functions are used for optimization experiments, and ISSA converges faster, more accurately, and with better robustness than other swarm intelligence algorithms. Finally, ISSA is used to optimize the SVM parameters and establish the ISSA-SVM fault diagnosis model. In the Sallen-key test circuit diagnosis experiments, the correct fault diagnosis rates of SSA-SVM and ISSA-SVM are 97.41% and 98.15%, respectively. The results show that the optimized ISSA-SVM model has a good analog circuit fault diagnosis with an increase in diagnostic accuracy.

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.

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.

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.

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.

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.

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]'.

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 Q3 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.

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.

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We investigated the changes of soil nutrients and plant communities in the artificial sand fixation forests of Caragana korshinskii with different ages. The results showed that soil organic carbon and soil total nitrogen contents increased with the stand ages, and were significantly higher in 40 and 50 year-old than other ages. Soil organic carbon and total nitrogen contents recovered much faster in the surface layer (0-10 cm) than in others. Soil nutrient stoichiometric ratios (C:P, N:P) in the 0-10 cm soil layer differed significantly among different stand ages. With the increases of stand age, C and N contents in C. korshinskii leaves increased significantly, and reached the maximum at 50 year-old. Leaf P content increased first and then decreased, being maximum at 18 year-old. Leaf C:N first increased and then decreased, being maximum at 12 year-old. The contents of photosynthetic pigments and leaf C:P and N:P decreased first and then increased, being minimum at 18 year-old. C. korshinskii was mainly influenced by N availability before 40 year-old, but mainly limited by P after. The species number, density, and vegetation cover of annual and perennial herbaceous plants increased with stand ages, and the community shifted from a simple shrub plant community to a complex shrub-herb community. The biomass of C. korshinskii and herbaceous plants increased significantly with stand age, and had a significant positive correlation with the contents of soil organic carbon, total nitrogen and N:P.

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.

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.