Precision pore structure optimization of additive manufacturing porous tantalum scaffolds for bone regeneration: A proof-of-concept study.

Currently, the treatment of bone defects in arthroplasty is a challenge in clinical practice. Nonetheless, commercially available orthopaedic scaffolds have shown limited therapeutic effects for large bone defects, especially for massiveand irregular defects. Additively manufactured porous tantalum, in particular, has emerged as a promising material for such scaffolds and is widely used in orthopaedics for its exceptional biocompatibility, osteoinduction, and mechanical properties. Porous tantalum has also exhibited unique advantages in personalised rapid manufacturing, which allows for the creation of customised scaffolds with complex geometric shapes for clinical applications at a low cost and high efficiency. However, studies on the effect of the pore structure of additively manufactured porous tantalum on bone regeneration have been rare. In this study, our group designed and fabricated a batch of precision porous tantalum scaffolds via laser powder bed fusion (LPBF) with pore sizes of 250 µm (Ta 250), 450 µm (Ta 450), 650 µm (Ta 650), and 850 µm (Ta 850). We then performed a series of in vitro experiments and observed that all four groups showed good biocompatibility. In particular, Ta 450 demonstrated the best osteogenic performance. Afterwards, our team used a rat bone defect model to determine the in vivo osteogenic effects. Based on micro-computed tomography and histology, we identified that Ta 450 exhibited the best bone ingrowth performance. Subsequently, sheep femur and hip defect models were used to further confirm the osteogenic effects of Ta 450 scaffolds. Finally, we verified the aforementioned in vitro and in vivo results via clinical application (seven patients waiting for revision total hip arthroplasty) of the Ta 450 scaffold. The clinical results confirmed that Ta 450 had satisfactory clinical outcomes up to the 12-month follow-up. In summary, our findings indicate that 450 µm is the suitable pore size for porous tantalum scaffolds. This study may provide a new therapeutic strategy for the treatment of massive, irreparable, and protracted bone defects in arthroplasty.

Decreased cold-inducible RNA-binding protein (CIRP) binding to GluRl on neuronal membranes mediates memory impairment resulting from prolonged hypobaric hypoxia exposure.

AIM: To investigate the molecular mechanisms underlying memory impairment induced by high-altitude (HA) hypoxia, specifically focusing on the role of cold-inducible RNA-binding protein (CIRP) in regulating the AMPA receptor subunit GluR1 and its potential as a therapeutic target. METHODS: A mouse model was exposed to 14 days of hypobaric hypoxia (HH), simulating conditions at an altitude of 6000 m. Behavioral tests were conducted to evaluate memory function. The expression, distribution, and interaction of CIRP with GluR1 in neuronal cells were analyzed. The binding of CIRP to GluR1 mRNA and its impact on GluR1 protein expression were examined. Additionally, the role of CIRP in GluR1 regulation was assessed using Cirp knockout mice. The efficacy of the Tat-C16 peptide, which consists of the Tat sequence combined with the CIRP 110-125 amino acid sequence, was also tested for its ability to mitigate HH-induced memory decline. RESULTS: CIRP was primarily localized in neurons, with its expression significantly reduced following HH exposure. This reduction was associated with decreased GluR1 protein expression on the cell membrane and increased localization in the cytoplasm. The interaction between CIRP and GluR1 was diminished under HH conditions, leading to reduced GluR1 stability on the cell membrane and increased cytoplasmic relocation. These changes resulted in a decreased number of synapses and dendritic spines, impairing learning and memory functions. Administration of the Tat-C16 peptide effectively ameliorated these impairments by modulating GluR1 expression and distribution in HH-exposed mice. CONCLUSION: CIRP plays a critical role in maintaining synaptic integrity under hypoxic conditions by regulating GluR1 expression and distribution. The Tat-C16 peptide shows promise as a therapeutic strategy for alleviating cognitive decline associated with HA hypoxia.

Causal Enhanced Drug-Target Interaction Prediction based on Graph Generation and Multi-Source Information Fusion.

MOTIVATION: The prediction of drug-target interaction is a vital task in the biomedical field, aiding in the discovery of potential molecular targets of drugs and the development of targeted therapy methods with higher efficacy and fewer side effects. Although there are various methods for drug-target interaction (DTI) prediction based on heterogeneous information networks, these methods face challenges in capturing the foundamental interaction between drugs and targets and ensuring the interpretability of the model. Moreover, they need to construct meta-paths artificially or a lot of feature engineering (prior knowledge), and graph generation can fuse information more flexibly without meta-path selection. RESULTS: We propose a causal enhanced method for drug-target interaction (CE-DTI) prediction that integrates graph generation and multi-source information fusion. First, we represent drugs and targets by modeling the fusion of their multi-source information through automatic graph generation. Once drugs and targets are combined, a network of drug-target pairs is constructed, transforming the prediction of drug-target interactions into a node classification problem. Specifically, the influence of surrounding nodes on the central node is separated into two groups: Causal and non-causal variable nodes. Causal variable nodes significantly impact the central node's classification, while non-causal variable nodes do not. Causal invariance is then used to enhance the contrastive learning of the drug-target pairs network. Our method demonstrates excellent performance compared to other competitive benchmark methods across multiple datasets. At the same time, the experimental results also show that the causal enhancement strategy can explore the potential causal effects between DTPs, and discover new potential targets. Additionally, case studies demonstrate that this method can identify potential drug targets. AVAILABILITY AND IMPLEMENTATION: The soure code of AdaDR is available at: Https://github.com/catly/CE-DTI.

Automatically detecting anchor cells and clustering for scRNA-seq data using scTSNN.

Advancing in single-cell RNA sequencing techniques enhances the resolution of cell heterogeneity study. Density-based unsupervised clustering has the potential to detect the representative anchor points and the number of clusters automatically. Meanwhile, discovering the true cell type of scRNA-seq data in the unsupervised scenario is still challenging. To this end, we proposed a tensor shared nearest neighbor anchor clustering for scRNA-seq data, named scTSNN, which first makes use of the tensor affinity learning module to mine the local-global balanced topological structures among cells, next designs density-based shared nearest neighbor measurement method to automatically detect anchor cells, finally partitions the non-anchor cells to obtain the clustering results. Validated on synthetic datasets and scRNA-seq datasets, scTSNN not only exactly detects the complicated structures but also has better performance in accuracy and robustness compared with the state-of-the-art methods. Moreover, case studies on mammalian cells and cervical cancer tumor cells demonstrate the selected anchor cells of scTSNN benefit the cell pseudotime inference and rare cell identification, which show good application and research value of scTSNN.

Effects of Detoxifying Substances on Uranium Removal by Bacteria Isolated from Mine Soils: Performance, Mechanisms, and Bacterial Communities.

In this study, we investigated the effect of detoxifying substances on U(VI) removal by bacteria isolated from mine soil. The results demonstrated that the highest U(VI) removal efficiency (85.6%) was achieved at pH 6.0 and a temperature of 35 °C, with an initial U(VI) concentration of 10 mg/L. For detoxifying substances, signaling molecules acyl homoserine lactone (AHLs, 0.1 µmol/L), anthraquinone-2, 6-disulfonic acid (AQDS, 1 mmol/L), reduced glutathione (GSH, 0.1 mmol/L), selenium (Se, 1 mg/L), montmorillonite (MT, 1 g/L), and ethylenediaminetetraacetic acid (EDTA, 0.1 mmol/L) substantially enhanced the bacterial U(VI) removal by 34.9%, 37.4%, 54.5%, 35.1%, 32.8%, and 47.8% after 12 h, respectively. This was due to the alleviation of U(VI) toxicity in bacteria through detoxifying substances, as evidenced by lower malondialdehyde (MDA) content and higher superoxide dismutase (SOD) and catalase (CAT) activities for bacteria exposed to U(VI) and detoxifying substances, compared to those exposed to U(VI) alone. FTIR results showed that hydroxyl, carboxyl, phosphorus, and amide groups participated in the U(VI) removal. After exposure to U(VI), the relative abundances of Chryseobacterium and Stenotrophomonas increased by 48.5% and 12.5%, respectively, suggesting their tolerance ability to U(VI). Gene function prediction further demonstrated that the detoxifying substances AHLs alleviate U(VI) toxicity by influencing bacterial metabolism. This study suggests the potential application of detoxifying substances in the U(VI)-containing wastewater treatment through bioremediation.

Extracellular volume fraction of liver and pancreas using spectral CT in hypertensive patients: A comparative study.

BACKGROUND: Besides the direct impact on the cardiovascular system, hypertension is closely associated with organ damage in the kidneys, liver, and pancreas. Chronic liver and pancreatic damage in hypertensive patients may be detectable via imaging. OBJECTIVE: To explore the correlation between hypertension-related indicators and extracellular volume fraction (ECV) of liver and pancreas measured by iodine maps, and to evaluate corresponding clinical value in chronic damage of liver and pancreas in hypertensive patients. METHODS: A prospective study from June to September 2023 included abdominal patients who underwent contrast-enhanced spectral CT. Normal and various grades of hypertensive blood pressure groups were compared. Upper abdominal iodine maps were constructed, and liver and pancreatic ECVs calculated. Kruskal-Wallis and Spearman analyses evaluated ECV differences and correlations with hypertension indicators. RESULTS: In 300 patients, hypertensive groups showed significantly higher liver and pancreatic ECV than the normotensive group, with ECV rising alongside hypertension severity. ECVliver displayed a stronger correlation with hypertension stages compared to ECVpancreas. Regression analysis identified hypertension severity as an independent predictor for increased ECV. CONCLUSIONS: ECVliver and ECVpancreas positively correlates with hypertension indicators and serves as a potential clinical marker for chronic organ damage due to hypertension, with ECVliver being more strongly associated than ECVpancreas.

Pomegranate peel extract incorporated soy protein isolate/Artemisia sphaerocephala Krasch. gum composite films for fresh-cut apples preservation.

The objective of this study was to prepare an active packaging film using phosphorylated soy protein isolate (PPS) and Artemisia sphaerocephala Krasch. gum (ASKG) as film matrices, with the incorporation of pomegranate peel extract (PPE) to preserve fresh-cut apples. The results showed that PA-PPE (PPS/ASKG-PPE) films significantly increased thickness by 24.47 %, tensile strength by 58.76 %, and elongation at break by 30.48 %. Additionally, water vapor permeability and oxygen permeability decreased significantly to 6.17 × 10-13 and 0.62 × 10-13 Kg•m•m-2•s-1•Pa-1, respectively. FTIR, XRD, and SEM analyses confirmed the formation of intermolecular hydrogen bonds between PPS, ASKG, and polyphenols extracted from pomegranate peel, indicating excellent compatibility. Furthermore, radical scavenging activity experiments demonstrated that these films exhibited a remarkable ability to scavenge DPPH and ABTS+ radicals up to 70.44 % and 74.28 %, respectively, when the PPE content was at 3 wt%. Moreover, PPS could achieve a sustained release effect on polyphenols with a relatively low release rate (63.83 %) even after seven days' time elapsed. Finally, the PA-PPE film displayed superior performance in reducing the weight loss and browning index of fresh-cut apples within 24 h of storage. The development of PA-PPE film could promote sustainable resource protection and demonstrate promising prospects in the field of fresh-cut fruit packaging.

DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temperate phages. Classifying virulent and temperate phages is crucial for further understanding of the phage-host interactions. Although there are several methods designed for phage lifestyle classification, they merely either consider sequence features or gene features, leading to low accuracy. A new computational method, DeePhafier, is proposed to improve classification performance on phage lifestyle. Built by several multilayer self-attention neural networks, a global self-attention neural network, and being combined by protein features of the Position Specific Scoring Matrix matrix, DeePhafier improves the classification accuracy and outperforms two benchmark methods. The accuracy of DeePhafier on five-fold cross-validation is as high as 87.54% for sequences with length >2000bp.

Spectroscopic evidence of spin-state excitation in d-electron correlated semiconductor FeSb2.

Iron antimonide (FeSb2) has been investigated for decades due to its puzzling electronic properties. It undergoes the temperature-controlled transition from an insulator to an ill-defined metal, with a cross-over from diamagnetism to paramagnetism. Extensive efforts have been made to uncover the underlying mechanism, but a consensus has yet to be reached. While macroscopic transport and magnetic measurements can be explained by different theoretical proposals, the essential spectroscopic evidence required to distinguish the physical origin is missing. In this paper, through the use of X-ray absorption spectroscopy and atomic multiplet simulations, we have observed the mixed spin states of 3d 6 configuration in FeSb2. Furthermore, we reveal that the enhancement of the conductivity, whether induced by temperature or doping, is characterized by populating the high-spin state from the low-spin state. Our work constitutes vital spectroscopic evidence that the electrical/magnetical transition in FeSb2 is directly associated with the spin-state excitation.

SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies.

Structural variation (SV) is an important form of genomic variation that influences gene function and expression by altering the structure of the genome. Although long-read data have been proven to better characterize SVs, SVs detected from noisy long-read data still include a considerable portion of false-positive calls. To accurately detect SVs in long-read data, we present SVDF, a method that employs a learning-based noise filtering strategy and an SV signature-adaptive clustering algorithm, for effectively reducing the likelihood of false-positive events. Benchmarking results from multiple orthogonal experiments demonstrate that, across different sequencing platforms and depths, SVDF achieves higher calling accuracy for each sample compared to several existing general SV calling tools. We believe that, with its meticulous and sensitive SV detection capability, SVDF can bring new opportunities and advancements to cutting-edge genomic research.

scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) enables the exploration of biological heterogeneity among different cell types within tissues at a resolution. Inferring cell types within tissues is foundational for downstream research. Most existing methods for cell type inference based on scRNA-seq data primarily utilize highly variable genes (HVGs) with higher expression levels as clustering features, overlooking the contribution of HVGs with lower expression levels. To address this, we have designed a novel cell type inference method for scRNA-seq data, termed scLEGA. scLEGA employs a novel zero-inflated negative binomial (ZINB) loss function that fully considers the contribution of genes with lower expression levels and combines two distinct scRNA-seq clustering strategies through a multi-head attention mechanism. It utilizes a low-expression optimized denoising autoencoder, based on the novel ZINB model, to extract low-dimensional features and handle dropout events, and a GCN-based graph autoencoder (GAE) that leverages neighbor information to guide dimensionality reduction. The iterative fusion of denoising and topological embedding in scLEGA facilitates the acquisition of cluster-friendly cell representations in the hidden embedding, where similar cells are brought closer together. Compared to 12 state-of-the-art cell type inference methods on 15 scRNA-seq datasets, scLEGA demonstrates superior performance in clustering accuracy, scalability, and stability. Our scLEGA model codes are freely available at https://github.com/Masonze/scLEGA-main.

Exploration of effective biomarkers for venous thrombosis embolism in Behçet's disease based on comprehensive bioinformatics analysis.

Behçet's disease (BD) is a multifaceted autoimmune disorder affecting multiple organ systems. Vascular complications, such as venous thromboembolism (VTE), are highly prevalent, affecting around 50% of individuals diagnosed with BD. This study aimed to identify potential biomarkers for VTE in BD patients. Three microarray datasets (GSE209567, GSE48000, GSE19151) were retrieved for analysis. Differentially expressed genes (DEGs) associated with VTE in BD were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, potential diagnostic genes were explored through protein-protein interaction (PPI) network analysis and machine learning algorithms. A receiver operating characteristic (ROC) curve and a nomogram were constructed to evaluate the diagnostic performance for VTE in BD patients. Furthermore, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate potential underlying mechanisms. Finally, the efficacy of listed drugs was assessed based on the identified signature genes. The limma package and WGCNA identified 117 DEGs related to VTE in BD. A PPI network analysis then selected 23 candidate hub genes. Four DEGs (E2F1, GATA3, HDAC5, and MSH2) were identified by intersecting gene sets from three machine learning algorithms. ROC analysis and nomogram construction demonstrated high diagnostic accuracy for these four genes (AUC: 0.816, 95% CI: 0.723-0.909). Immune cell infiltration analysis revealed a positive correlation between dysregulated immune cells and the four hub genes. ssGSEA provided insights into potential mechanisms underlying VTE development and progression in BD patients. Additionally, therapeutic agent screening identified potential drugs targeting the four hub genes. This study employed a systematic approach to identify four potential hub genes (E2F1, GATA3, HDAC5, and MSH2) and construct a nomogram for VTE diagnosis in BD. Immune cell infiltration analysis revealed dysregulation, suggesting potential macrophage involvement in VTE development. ssGSEA provided insights into potential mechanisms underlying BD-induced VTE, and potential therapeutic agents were identified.

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.

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.

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.

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.

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.

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.