Spatiotemporal orchestration of macrophage activation trajectories by Vγ4 T cells during skin wound healing.

Dysregulated macrophage polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotypes underlies impaired cutaneous wound healing. This study reveals Vγ4+ γδ T cells spatiotemporally calibrate macrophage trajectories during skin repair via sophisticated interferon-γ (IFN-γ) conditioning across multiple interconnected tissues. Locally within wound beds, infiltrating Vγ4+ γδ T cells directly potentiate M1 activation and suppress M2 polarization thereby prolonging local inflammation. In draining lymph nodes, infiltrated Vγ4+ γδ T cells expand populations of IFN-γ-competent lymphocytes which disseminate systemically and infiltrate into wound tissues, further enforcing M1 macrophages programming. Moreover, Vγ4+γδ T cells flushed into bone marrow stimulate increased IFN-γ production, which elevates the output of pro-inflammatory Ly6C+monocytes. Mobilization of these monocytes continually replenishes the M1 macrophage pool in wounds, preventing phenotypic conversion to M2 activation. Thus, multi-axis coordination of macrophage activation trajectories by trafficking Vγ4+ γδ T cells provides a sophisticated immunological mechanism regulating inflammation timing and resolution during skin repair.

Optimal response to tislelizumab plus chemotherapy in metastatic triple-negative breast cancer: a case report and literature review.

Metastatic triple-negative breast cancer (mTNBC) has the worst prognosis among breast cancer subtypes. Immune checkpoint inhibitors (ICIs) plus chemotherapy have promising survival benefits. Herein, we report a 51-year-old woman whose metastatic lesions were diagnosed as triple-negative subtype and who received tislelizumab plus eribulin treatment and achieved excellent efficacy. To our knowledge, this study is the first attempt to present tislelizumab in combination with eribulin for mTNBC treatment. New treatments resulting in prolonged survival and durable clinical responses would benefit mTNBC patients. Then, we summarize the possible influencing factors of the interaction between tislelizumab and eribulin.

Application of one-dimensional hierarchical network assisted screening for cervical cancer based on Raman spectroscopy combined with attention mechanism.

Cervical cancer is one of the most common malignant tumors among women, and its pathological change is a relatively slow process. If it can be detected in time and treated properly, it can effectively reduce the incidence rate and mortality rate of cervical cancer, so the early screening of cervical cancer is particularly critical and significant. In this paper, we used Raman spectroscopy technology to collect the tissue sample data of patients with cervicitis, Low-grade Squamous Intraepithelial Lesion, High-grade Squamous Intraepithelial Lesion, Well differentiated squamous cell carcinoma, Moderately differentiated squamous cell carcinoma, Poorly differentiated squamous cell carcinoma and cervical adenocarcinoma. A one-dimensional hierarchical convolutional neural network based on attention mechanism was constructed to classify and identify seven types of tissue samples. The attention mechanism Efficient Channel Attention Networks module and Squeeze-and-Excitation Networks module were combined with the established one-dimensional convolutional hierarchical network model, and the results showed that the combined model had better diagnostic performance. The average accuracy, F1, and AUC of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model after 5-fold cross validations could reach 96.49%±2.12%, 0.9663±0.0253, and 0.9815±0.0224, respectively, which were 1.58%, 0.0140, and 0.008 higher than those of hierarchical network. The recall rate of the Principal Component Analysis-Efficient Channel Attention-hierarchical network model was as high as 96.78%±2.85%, which is 1.47% higher than hierarchical network. Compared with the classification results of traditional CNN and ResNet for seven types of cervical cancer staging, the accuracy of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model is 3.33% and 11.05% higher, respectively. The experimental results indicate that the model established in this study is easy to operate and has high accuracy. It has good reference value for rapid screening of cervical cancer, laying a foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer.

Prognostic effects of RASSF1A, BRCA1, APC, and p16 promoter methylation in ovarian cancer: a meta-analysis.

INTRODUCTION: DNA methylation plays an important role in the carcinogenesis, progression, and prognosis of various human cancers. RASSF1A, BRCA1, APC, and p16 are the frequently methylated genes among patients with ovarian cancer. Therefore, our study aimed to better determine the prognostic and cancer characteristics effects of RASSF1A, BRCA1, APC, and p16 promoter methylation in ovarian cancer patients. METHODS: Databases such as PubMed, Web of Science, EMBASE, CNKI, and WanFang were searched for published studies up to March 4, 2024. The outcomes are shown as OR and HR with their 95% CIs. Then, the random or fixed effect model was performed to evaluate the effect sizes. RESULTS: Finally, 27 articles were included in this meta-analysis. No significant relationships were observed between RASSF1A, BRCA1, and APC promoter methylation and the clinical prognostic (including overall survival and progression-free survival) and cancer characteristics (including ascites, lymph node metastasis, and pelvic peritoneal metastasis) in ovarian cancer. p16 promoter methylation was significantly related to poor PFS (HR=1.52, 95% CI=1.14 to 2.04) and OS (HR=1.39, 95% CI=1.06, to 1.83) in univariate, and poor PFS in multivariate Cox regression models (HR =1.42, 95% CI=1.05 to1.92). Besides, our results indicated that the clinical stage was associated with inferior OS while there was no significant association between tumor grade and OS. CONCLUSION: RASSF1A, BRCA1, and APC promoter methylation were not significantly associated with clinical prognostic and cancer characteristics. P16 may be a useful biomarker for predicting PFS in ovarian cancer. Furthermore, the clinical stage was significantly associated with OS. In further research, more prospective and multicenter validation studies remain needed.

Macrophages in necrotic liver lesion repair: opportunities for therapeutical applications.

Healthy livers contain 80% of body resident macrophages known as Kupffer cells. In diseased livers, the number of Kupffer cells usually drops, but is compensated by infiltration of monocyte-derived macrophages, which can differentiate into Kupffer-like cells. Early studies suggest that Kupffer cells play important roles in both promoting liver injury as well as liver regeneration. Yet, the distinction of the functionalities from resident and infiltrating macrophages is not always made. By using more specific macrophage markers and targeted cell depletion and single cell RNA sequencing, recent studies revealed several subsets of monocyte-derived macrophages that play important functions in inducing liver damage and inflammation as well as in liver repair and regeneration. In this review, we discuss the different roles that hepatic macrophages play in promoting necrotic liver lesion resolution and dead cell clearance, as well as the targeting of these cells as potential tools for the development of novel therapies for acute liver failure and acute-on-chronic liver failure.

A method for accurate identification of Uyghur medicinal components based on Raman spectroscopy and multi-label deep learning.

Uyghur medicine is one of the four major ethnic medicines in China and is a component of traditional Chinese medicine. The intrinsic quality of Uyghur medicinal materials will directly affect the clinical efficacy of Uyghur medicinal preparations. However, in recent years, problems such as adulteration of Uyghur medicinal materials and foreign bodies with the same name still exist, so it is necessary to strengthen the quality control of Uyghur medicines to guarantee Uyghur medicinal efficacy. Identifying the components of Uyghur medicines can clarify the types of medicinal materials used, is a crucial step to realizing the quality control of Uyghur medicines, and is also an important step in screening the effective components of Uyghur medicines. Currently, the method of identifying the components of Uyghur medicines relies on manual detection, which has the problems of high toxicity of the unfolding agent, poor stability, high cost, low efficiency, etc. Therefore, this paper proposes a method based on Raman spectroscopy and multi-label deep learning model to construct a model Mix2Com for accurate identification of Uyghur medicine components. The experiments use computer-simulated mixtures as the dataset, introduce the Long Short-Term Memory Model (LSTM) and Attention mechanism to encode the Raman spectral data, use multiple parallel networks for decoding, and ultimately realize the macro parallel prediction of medicine components. The results show that the model is trained to achieve 90.76% accuracy, 99.41% precision, 95.42% recall value and 97.37% F1 score. Compared to the traditional XGBoost model, the method proposed in the experiment improves the accuracy by 49% and the recall value by 18%; compared with the DeepRaman model, the accuracy is improved by 9% and the recall value is improved by 14%. The method proposed in this paper provides a new solution for the accurate identification of Uyghur medicinal components. It helps to improve the quality standard of Uyghur medicinal materials, advance the research on screening of effective chemical components of Uyghur medicines and their action mechanisms, and then promote the modernization and development of Uyghur medicine.

Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study.

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung cancer (NSCLC), and identifying candidates likely responding to this advanced treatment is of important clinical significance. The current multi-institutional study aims to develop a deep learning model to predict pathologic complete response (pCR) to neoadjuvant immunotherapy in NSCLC based on computed tomography (CT) imaging and further prob the biologic foundation of the proposed deep learning signature. A total of 248 participants administrated with neoadjuvant immunotherapy followed by surgery for NSCLC at Ruijin Hospital, Ningbo Hwamei Hospital, and Affiliated Hospital of Zunyi Medical University from January 2019 to September 2023 were enrolled. The imaging data within 2 weeks prior to neoadjuvant chemoimmunotherapy were retrospectively extracted. Patients from Ruijin Hospital were grouped as the training set (n = 104) and the validation set (n = 69) at the 6:4 ratio, and other participants from Ningbo Hwamei Hospital and Affiliated Hospital of Zunyi Medical University served as an external cohort (n = 75). For the entire population, pCR was obtained in 29.4% (n = 73) of cases. The areas under the curve (AUCs) of our deep learning signature for pCR prediction were 0.775 (95% confidence interval [CI]: 0.649 - 0.901) and 0.743 (95% CI: 0.618 - 0.869) in the validation set and the external cohort, significantly superior than 0.579 (95% CI: 0.468 - 0.689) and 0.569 (95% CI: 0.454 - 0.683) of the clinical model. Furthermore, higher deep learning scores correlated to the upregulation for pathways of cell metabolism and more antitumor immune infiltration in microenvironment. Our developed deep learning model is capable of predicting pCR to neoadjuvant chemoimmunotherapy in patients with NSCLC.

Sandwich-like CuNPs@AgNPs@PSB SERS substrates for sensitive detection of R6G and Forchlorfenuron.

The development of a highly sensitive, synthetically simple and economical SERS substrate is technically very important. A fast, economical, sensitive and reproducible CuNPs@AgNPs@ Porous silicon Bragg reflector (PSB) SERS substrate was prepared by electrochemical etching and in situ reduction method. The developed CuNPs@AgNPs@PSB has a large specific surface area and abundant "hot spot" region, which makes the SERS performance excellent. Meanwhile, the successful synthesis of CuNPs@AgNPs can not only modulate the plasmon resonance properties of nanoparticles, but also effectively prolong the time stability of Cu nanoparticles. The basic performance of the substrate was evaluated using rhodamine 6G (R6G). (Detection limit reached 10-15 M, R2 = 0.9882, RSD = 5.3 %) The detection limit of Forchlorfenuron was 10 µg/L. The standard curve with a regression coefficient of 0.979 was established in the low concentration range of 10 µg/L -100 µg/L. This indicates that the prepared substrates can accomplish the detection of pesticide residues in the low concentration range. The prepared high-performance and high-sensitivity SERS substrate have a very promising application in detection technology.

SLE diagnosis research based on SERS combined with a multi-modal fusion method.

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Synthesis of Quillaic Acid through Sustainable C-H Bond Activations.

To meet the demand for quillaic acid, a multigram synthesis of quillaic acid was accomplished in 14 steps, starting from oleanolic acid, leading to an overall yield of 3.4%. Key features include C-H activation at C-16 and C-23. Through Pd-catalyzed C-H acetoxylation, the oxidation at C-23 was observed as the major product, as opposed to at C-24. A copper-mediated C-H hydroxylation using O2 successfully afforded the single isomer, 16ß-ol triterpenoid, followed by configuration inversion to the desired 16α-ol compound. In summary, with steps optimized and conducted on a multigram scale, quillaic acid could be feasibly acquired through C-H activation with inexpensive copper catalysts, promoting a more sustainable approach.

Latent tuberculosis infection and infection-associated risk factors for miner workers with silicosis in eastern China.

OBJECTIVES: Silicosis people are at high risk of developing pulmonary tuberculosis. Whether silica exposure increases the likelihood of latent tuberculosis infection (LTBI) was not well understood, and potential factors involved in LTBI risk among silicosis people were not evaluated before. Thus, LTBI among silicosis people and potential risk factors for LTBI among silicosis people were evaluated in this study. METHODS: A cross-sectional study was undertaken for 130 miner workers with silicosis. The QFT-GIT was performed for LTBI detection. RESULTS: The LTBI was high to 31.6% (36/114) for silicosis participants, and 13.1% (13/99) had a history of tuberculosis. Drinking was associated with LTBI risk (OR = 6.92, 95%CI, 1.47-32.66, P = 0.015). Meanwhile, tunneling work was associated with an increased risk of LTBI compared with other mining occupations (OR = 3.91,95%CI,1.20-12.70, P = 0.024). CONCLUSIONS: The LTBI rate of silicosis participants was high and more than 10% had a history of tuberculosis. Drinking alcohol and tunneling were independent risk factors for LTBI in silicosis participants.

Assessing the role of surface layer and molecular probe size in diffusion within meniscus tissue.

Diffusion within extracellular matrix is essential to deliver nutrients and larger metabolites to the avascular region of the meniscus. It is well known that both structure and composition of the meniscus vary across its regions; therefore, it is crucial to fully understand how the heterogenous meniscal architecture affects its diffusive properties. The objective of this study was to investigate the effect of meniscal region (core tissue, femoral, and tibial surface layers) and molecular weight on the diffusivity of several molecules in porcine meniscus. Tissue samples were harvested from the central area of porcine lateral menisci. Diffusivity of fluorescein (MW 332 Da) and three fluorescence-labeled dextrans (MW 3k, 40k, and 150k Da) was measured via fluorescence recovery after photobleaching. Diffusivity was affected by molecular size, decreasing as the Stokes' radius of the solute increased. There was no significant effect of meniscal region on diffusivity for fluorescein, 3k and 40k dextrans (p>0.05). However, region did significantly affect the diffusivity of 150k Dextran, with that in the tibial surface layer being larger than in the core region (p = 0.001). Our findings contribute novel knowledge concerning the transport properties of the meniscus fibrocartilage. This data can be used to advance the understanding of tissue pathophysiology and explore effective approaches for tissue restoration.

Refraction and topographic risk factors for early myopic regression after small-incision lenticule extraction surgery.

We sought to evaluate the topographic risk factors for early myopic regression after small-incision lenticule extraction (SMILE). A retrospective case‒control study was conducted, and individuals who underwent SMILE surgery were enrolled. Among them, 406 and 14 eyes were categorized into the nonregression and regression groups, respectively. The preoperative and postoperative parameters in the two groups were collected, including spherical refraction (SE), axial length (AXL) and topographic data. A generalized linear model was adopted to analyze the difference in each parameter between the two groups. After 6 months, UCVA decreased in the regression group, and SE increased in the regression group (both P < 0.05). The increase in the CCT at the thinnest point (P = 0.044), flat corneal curvature (P = 0.012) and TCRP (P = 0.001) were significantly greater in the regression group. Regarding the risk factors for myopic regression, preoperative SE, preoperative sphere power, preoperative AXL, preoperative flat corneal curvature, preoperative SA, early postoperative SE, early postoperative sphere power, early postoperative AXL and early postoperative CCT difference were significantly greater in the regression group (all P < 0.05). The SE, sphere power, AXL, preoperative flat corneal curvature, preoperative SA, and postoperative CCT difference correlate with early myopic regression after SMILE.

Cerebral blood flow changes in maintenance hemodialysis patients with restless legs syndrome and their clinical significance:a cross-sectional case-control study.

OBJECTIVE: Restless legs syndrome (RLS) stands as a prevalent neurological complication within maintenance hemodialysis (MHD) patients. However, the alterations in cerebral blood flow (CBF) among MHD-RLS patients remain uncharted. Through the utilization of the arterial spin labeling (ASL) technique, we evaluated the fluctuations in CBF within distinct brain regions and analyzed the risk factors for the development of RLS in MHD patients in the context of the clinic. METHODS: Thirty-one MHD patients with concomitant RLS (MHD-RLS group) and thirty-one non-RLS patients matched based on age, gender, as well as cognitive function (MHD-nRLS group) were included. Through image preprocessing and data analysis, the changes in CBF values in distinct brain regions were obtained, and the CBF values of brain regions with substantial differences between the two groups were correlated with the RLS scores. Furthermore, the differences in baseline data were compared, and through the utilization of multifactorial logistic regression, the independent risk factors for the development of RLS were examined. RESULTS: Compared with the MHD-nRLS group, the MHD-RLS group had increased CBF in the right superior temporal gyrus, reduced CBF in the right hippocampus, left middle frontal gyrus, inferior frontal gyrus of right triangle, middle frontal gyrus of left orbit, left precentral gyrus, and left precuneus. Only left precentral gyrus CBF were negatively correlated with RLS scores after correction for dialysis duration(r = -0.436, P = 0.016). Accordingly, multifactorial regression analysis by stepwise method yielded that the left precentral gyrus CBF values(OR: 0.968, 95%CI: 0.944-0.993, P = 0.012) remained an independent risk factor for RLS in MHD patients. In addition, the results showed that hemodialysis duration (OR: 1.055, 95%CI: 1.014-1.098, P = 0.008) and serum iron levels (OR: 0.685, 95%CI: 0.551-0.852, P = 0.001) were also risk factors for the development of RLS. CONCLUSION: Patients afflicted with MHD-RLS exhibit alterations in CBF across several brain regions. Notably, the left precentral gyrus might serve as a pivotal region influencing the onset of RLS among MHD patients. Furthermore, extended hemodialysis duration and a relative insufficiency in serum iron levels independently contribute as risk factors for RLS development within the MHD patient population.

Immunometabolic reprogramming of macrophages with inhalable CRISPR/Cas9 nanotherapeutics for acute lung injury intervention.

Acute lung injury (ALI) represents a critical respiratory condition typified by rapid-onset lung inflammation, contributing to elevated morbidity and mortality rates. Central to ALI pathogenesis lies macrophage dysfunction, characterized by an overabundance of pro-inflammatory cytokines and a shift in metabolic activity towards glycolysis. This study emphasizes the crucial function of glucose metabolism in immune cell function under inflammatory conditions and identifies hexokinase 2 (HK2) as a key regulator of macrophage metabolism and inflammation. Given the limitations of HK2 inhibitors, we propose the CRISPR/Cas9 system for precise HK2 downregulation. We developed an aerosolized core-shell liposomal nanoplatform (CSNs) complexed with CaP for efficient drug loading, targeting lung macrophages. Various CSNs were synthesized to encapsulate an mRNA based CRISPR/Cas9 system (mCas9/gHK2), and their gene editing efficiency and HK2 knockout were examined at both gene and protein levels in vitro and in vivo. The CSN-mCas9/gHK2 treatment demonstrated a significant reduction in glycolysis and inflammation in macrophages. In an LPS-induced ALI mouse model, inhaled CSN-mCas9/gHK2 mitigated the proinflammatory tumor microenvironment and reprogrammed glucose metabolism in the lung, suggesting a promising strategy for ALI prevention and treatment. This study highlights the potential of combining CRISPR/Cas9 gene editing with inhalation delivery systems for effective, localized pulmonary disease treatment, underscoring the importance of targeted gene modulation and metabolic reprogramming in managing ALI. STATEMENT OF SIGNIFICANCE: This study investigates an inhalable CRISPR/Cas9 gene editing system targeting pulmonary macrophages, with the aim of modulating glucose metabolism to alleviate Acute Lung Injury (ALI). The research highlights the role of immune cell metabolism in inflammation, as evidenced by changes in macrophage glucose metabolism and a notable reduction in pulmonary edema and inflammation. Additionally, observed alterations in macrophage polarization and cytokine levels in bronchoalveolar lavage fluid suggest potential therapeutic implications. These findings not only offer insights into possible ALI treatments but also contribute to the understanding of immune cell metabolism in inflammatory diseases, which could be relevant for various inflammatory and metabolic disorders.

Exosomal miR-151-3p in saliva: A potential non-invasive marker for gastric cancer diagnosis and prognosis modulated by Sijunzi decoction (SJZD) in mice.

Gastric cancer (GC) is one of the most prominent malignancies that originate in the epithelial cells of the gastric mucosa and is one of the main causes of cancer-related mortality worldwide. New circulating biomarkers of exosomal RNA might have great potential for non-invasive early prognosis of GC. Sijunzi Decoction (SJZD) is a typical representative formula of the method of benefiting Qi and strengthening the spleen in Traditional Chinese Medicine (TCM). However, the effects and mechanism of SJZD in treating GC remain unclear. This study looked for biomarkers of exosomal RNA for early prognosis of GC, and explored the mechanism of SJZD in treating GC. A gastric cancer model with spleen deficiency syndrome was established in nude mice, and the curative effects of SJZD were investigated. Differentially expressed miRNAs in plasma and saliva exosomes were sequenced and analyzed. Potential target genes of these miRNAs were predicted and applied for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment annotation. Overlapping miRNAs in saliva and plasma samples were analyzed, and qRT-PCR was performed for verification. miR-151a-3p was selected, and qRT-PCR further determined that miR-151a-3p was downregulated in saliva and plasma exosomes from the SJZD group. The intersected miR-151a-3p target genes were predicted and enriched in the extrinsic apoptotic signaling pathways. SJZD significantly ameliorates gastric cancer with spleen deficiency syndrome in mouse models, and exosomal miRNAs, particularly miR-151-3p, might be modulated by SJZD in plasma and saliva. The exosomal miR-151-3p in saliva may serve as a non-invasive potential marker for gastric cancer diagnosis and prognosis.

Causal Effect Estimation on Imaging and Clinical Data for Treatment Decision Support of Aneurysmal Subarachnoid Hemorrhage.

Aneurysmal subarachnoid hemorrhage is a serious medical emergency of brain that has high mortality and poor prognosis. Treatment effect estimation is of high clinical significance to support the treatment decision-making for aneurysmal subarachnoid hemorrhage. However, most existing studies on treatment decision support of this disease are unable to simultaneously compare the potential outcomes of different treatments for a patient. Furthermore, these studies fail to harmoniously integrate the imaging data with non-imaging clinical data, both of which are significant in clinical scenarios. In this paper, the key challenges we address are: how to effectively estimate the treatment effect for aneurysmal subarachnoid hemorrhage; and how to utilize multi-modality data to perform this estimation. Specifically, we first propose a novel scheme that uses multi-modality confounders distillation architecture to predict the treatment outcome and treatment assignment simultaneously. Notably, with these distilled confounder features, we design an imaging and non-imaging interaction representation learning strategy to use the complementary information extracted from different modalities to balance the feature distribution of different treatment groups. We have conducted extensive experiments using a clinical dataset of 656 subarachnoid hemorrhage cases, which was collected from the Hospital Authority of Hong Kong. Our method shows consistent improvements on the evaluation metrics of treatment effect estimation, achieving state-of-the-art results over strong competitors. Code is released at https://github.com/med-air/TOP-aSAH.