ACSL3 regulates breast cancer progression via lipid metabolism reprogramming and the YES1/YAP axis.

OBJECTIVE: Mitochondrial fatty acid oxidation is a metabolic pathway whose dysregulation is recognized as a critical factor in various cancers, because it sustains cancer cell survival, proliferation, and metastasis. The acyl-CoA synthetase long-chain (ACSL) family is known to activate long-chain fatty acids, yet the specific role of ACSL3 in breast cancer has not been determined. METHODS: We assessed the prognostic value of ACSL3 in breast cancer by using data from tumor samples. Gain-of-function and loss-of-function assays were also conducted to determine the roles and downstream regulatory mechanisms of ACSL3 in vitro and in vivo. RESULTS: ACSL3 expression was notably downregulated in breast cancer tissues compared with normal tissues, and this phenotype correlated with improved survival outcomes. Functional experiments revealed that ACSL3 knockdown in breast cancer cells promoted cell proliferation, migration, and epithelial-mesenchymal transition. Mechanistically, ACSL3 was found to inhibit ß-oxidation and the formation of associated byproducts, thereby suppressing malignant behavior in breast cancer. Importantly, ACSL3 was found to interact with YES proto-oncogene 1, a member of the Src family of tyrosine kinases, and to suppress its activation through phosphorylation at Tyr419. The decrease in activated YES1 consequently inhibited YAP1 nuclear colocalization and transcriptional complex formation, and the expression of its downstream genes in breast cancer cell nuclei. CONCLUSIONS: ACSL3 suppresses breast cancer progression by impeding lipid metabolism reprogramming, and inhibiting malignant behaviors through phospho-YES1 mediated inhibition of YAP1 and its downstream pathways. These findings suggest that ACSL3 may serve as a potential biomarker and target for comprehensive therapeutic strategies for breast cancer.

Liraglutide ameliorates high glucose-induced vascular endothelial injury through TRIB3/NF-κB signaling pathway.

As one of the most commonly used antidiabetic medications clinically, liraglutide is involved in the protection of vascular endothelium, and whether it can relieve high glucose-induced vascular endothelial damage was unknown. This study aims to address the response of liraglutide (LIRA) on human umbilical vein endothelial cells, as well as to elucidate its possible underlying mechanism. We established a vascular endothelial cell injury model by exposing human umbilical vein endothelial cells (HUVECs) to high glucose, and used LIRA pretreatment before HG treatment to address the endothelial protective effect of LIRA. Our results suggest that LIRA prevented HG-induced HUVEC apoptosis, oxidative stress, inflammasome activation, and pyroptosis. Furthermore, silencing of tribbles homolog 3 (TRIB3) could markedly reduce HG-induced HUVEC apoptosis, ROS level, the expressions of TXNIP, cleaved caspase3, NLRP3, and caspase1, indicating TRIB3 inhibition protected HUVECs against HG-induced vascular endothelial injury. In addition, LIRA restrained NF-κB/IκB-α signaling pathway activation in HUVECs. Thus, LIRA appears to mitigate HG-induced apoptosis, oxidative stress, inflammasome activation, and pyroptosis in HUVECs via regulating the TRIB3/NF-κB/IκB-α signaling pathway. Our study provides new insight into the mechanisms underlying the protective activity of LIRA against the vascular endothelial injury in diabetic vascular complication.

Operando-reconstructed polyatomic ion layers boost the activity and stability of industrial current-density water splitting.

Metal-organic frameworks have garnered attention as highly efficient pre-electrocatalysts for the oxygen evolution reaction (OER). Current structure-activity relationships primarily rely on the assumption that the complete dissolution of organic ligands occurs during electrocatalysis. Herein, modeling based on NiFe Prussian blue analogs (NiFe-PBAs) show that cyanide ligands leach from the matrix and subsequently oxidize to corresponding inorganic ions (ammonium and carbonate) that re-adsorb onto the surface of NiFe OOH during the OER process. Interestingly, the surface-adsorbed inorganic ions induce the OER reaction of NiFe OOH to switch from the adsorbate evolution to the lattice-oxygen-mediated mechanism, thus contributing to the high activity. In addition, this reconstructed inorganic ion layer acting as a versatile protective layer can prevent the dissolution of metal sites to maintain contact between catalytic sites and reactive ions, thus breaking the activity-stability trade-off. Consequently, our constructed NiFe-PBAs exhibit excellent durability for 1250 h with an ultralow overpotential of 253 mV at 100 mA cm-2. The scale-up NiFe-PBAs operated with a low energy consumption of ∼4.18 kWh m-3 H2 in industrial water electrolysis equipment. The economic analysis of the entire life cycle demonstrates that this green hydrogen production is priced at US$2.59/ [Formula: see text] , meeting global targets (

Phase I/II Clinical Study of PRaG Regimen Combined With Intraperitoneal Infusion of PD-1 Inhibitor for Advanced Refractory Solid Tumors With Cancerous Ascites (PRaG4.0P Study).

Objective: The prognosis of malignant tumors with peritoneal metastases and cancerous ascites has generally been poor, with limited treatment options. The PRaG regimen, which comprised of hypofractionated radiotherapy, programmed cell death-1 (PD-1) inhibitor, and granulocyte-macrophage colony-stimulating factor (GM-CSF), showed a survival advantage in patients with advanced solid tumors who failed at least the first line of standard systemic treatment. Intraperitoneal infusion of PD-1 inhibitors may be a novel therapeutic strategy for managing malignant ascites. Integrating the PRaG regimen with intraperitoneal perfusion of a PD-1 inhibitor might control malignant ascites and provide further survival benefits in these patients. This proposed study aims to investigate the safety and efficacy of intraperitoneal infusion of serplulimab in combination with the PRaG regimen in patients with simultaneous advanced solid tumors and cancerous ascites who fail at least the first-line treatment. Methods: This proposed study is a prospective, single-arm, open-label, multicenter clinical trial. All eligible patients will receive 2 cycles of intensive treatment, a combination of PRaG regimen with an intraperitoneal infusion of PD-1 inhibitor. The patients who are beneficially treated with intensive treatment will receive consolidation treatment every 2 weeks until ascites disappear, disease progression occurs, intolerable toxicity occurs, or for up to 1 year. Phase I of this study will be conducted using a modified 3 + 3 design. The dose of intraperitoneal infusion of PD-1 inhibitor for phase II will be determined according to dose-limiting toxicity evaluation in the phase I study. Conclusion: This prospective, open-label, multicenter study will potentially lead to intraperitoneal perfusion of a PD-1 inhibitor being a new strategy for malignant ascites patients and provide a meaningful efficacy and safety of the combination of PRaG regimen with an intraperitoneal infusion of PD-1 inhibitor for these patients.

Improving quantitative prediction of protein subcellular locations in fluorescence images through deep generative models.

Machine learning has been employed in recognizing protein localization at the subcellular level, which highly facilitates the protein function studies, especially for those multi-label proteins that localize in more than one organelle. However, existing works mostly study the qualitative classification of protein subcellular locations, ignoring fraction of one multi-label protein in different locations. Actually, about 50 % proteins are multi-label proteins, and the ignorance of quantitative information highly restricts the understanding of their spatial distribution and functional mechanism. One reason of the lack of quantitative study is the insufficiency of quantitative annotations. To address the data shortage problem, here we proposed a generative model, PLocGAN, which could generate cell images with conditional quantitative annotation of the fluorescence distribution. The model was a conditional generative adversarial network, in which the condition learning utilized partial label learning to overcome the lack of training labels and allowed training with only qualitative labels. Meanwhile, it used contrastive learning to enhance diversity of the generated images. We assessed the PLocGAN on four pixel-fused synthetic datasets and one real dataset, and demonstrated that the model could generate images with good fidelity and diversity, outperforming existing state-of-the-art generative methods. To verify the utility of PLocGAN in the quantitative prediction of protein subcellular locations, we replaced the training images with generated quantitative images and built prediction models, and found that they had a boosting effect on the quantitative estimation. This work demonstrates the effectiveness of deep generative models in bioimage analysis, and provides a new solution for quantitative subcellular proteomics.

The effect of mango aroma in low-sugar beverage: A sensory study on odor induced sweetness enhancement.

Excessive intake of sugar has become a public concern. However, it is challenging for food industries to decrease sugar level without sacrificing safety and sensory profile. Odor-induced sweetness enhancement (OISE) is believed to be a novel and promising strategy for sugar reduction. In order to investigate the OISE effect of mango aroma and evaluate its degree of sugar reduction in low-sugar beverages, a mathematical model was constructed through sensory evaluation in this study. The results showed that the maximum liking of low-sugar model beverages was 4.28 % sucrose and 0.57 % mango flavor. The most synergistic of OISE was at the concentration level of 2.24 % sucrose + 0.25 % mango flavor, which was equivalent to 2.96 % pure sucrose solution. With 32.14 % sugar reduction, the mango aroma was suggested to generate the OISE effect. However, the same level of garlic aroma was not able to enhance sweetness perception, suggesting that the congruency of aroma and taste is a prerequisite for the OISE effect to occur. This study demonstrated that the cross-modal interaction of mango aroma on sweetness enhancement in low-sugar model beverages could provide practical guidance for developing sugar-reduced beverages without applying sweeteners.

Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism.

Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses.

Smart Microneedle Arrays Integrating Cell-Free Therapy and Nanocatalysis to Treat Liver Fibrosis.

Liver fibrosis is a chronic pathological condition lacking specific clinical treatments. Stem cells, with notable potential in regenerative medicine, offer promise in treating liver fibrosis. However, stem cell therapy is hindered by potential immunological rejection, carcinogenesis risk, efficacy variation, and high cost. Stem cell secretome-based cell-free therapy offers potential solutions to address these challenges, but it is limited by low delivery efficiency and rapid clearance. Herein, an innovative approach for in situ implantation of smart microneedle (MN) arrays enabling precisely controlled delivery of multiple therapeutic agents directly into fibrotic liver tissues is developed. By integrating cell-free and platinum-based nanocatalytic combination therapy, the MN arrays can deactivate hepatic stellate cells. Moreover, they promote excessive extracellular matrix degradation by more than 75%, approaching normal levels. Additionally, the smart MN arrays can provide hepatocyte protection while reducing inflammation levels by ≈70-90%. They can also exhibit remarkable capability in scavenging almost 100% of reactive oxygen species and alleviating hypoxia. Ultimately, this treatment strategy can effectively restrain fibrosis progression. The comprehensive in vitro and in vivo experiments, supplemented by proteome and transcriptome analyses, substantiate the effectiveness of the approach in treating liver fibrosis, holding immense promise for clinical applications.

Discovery of a class of glycosaminoglycan lyases with ultrabroad substrate spectrum and their substrate structure preferences.

Glycosaminoglycan (GAG) lyases are often strictly substrate specific, and it is especially difficult to simultaneously degrade GAGs with different types of glycosidic bonds. Herein, we found a new class of GAG lyases (GAGases) from different bacteria. These GAGases belong to polysaccharide lyase 35 family and share quite low homology with the identified GAG lyases. The most surprising thing is that GAGases can not only degrade three types of GAGs: hyaluronan, chondroitin sulfate, and heparan sulfate but also even one of them can also degrade alginate. Further investigation of structural preferences revealed that GAGases selectively act on GAG domains composed of non/6-O-/N-sulfated hexosamines and d-glucoronic acids as well as on alginate domains composed of d-mannuronic acids. In addition, GAG lyases were once speculated to have evolved from alginate lyases, but no transitional enzymes have been found. The discovery of GAGases not only broadens the category of GAG lyases, provides new enzymatic tools for the structural and functional studies of GAGs with specific structures, but also provides candidates for the evolution of GAG lyases.

A comprehensive analysis of GAS2 family members identifies that GAS2L1 is a novel biomarker and promotes the proliferation of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common primary liver cancer with a high incidence and mortality. Members of the growth-arresting-specific 2 (GAS2) family are involved in various biological processes in human malignancies. To date, there is only a limited amount of information available about the expression profile and clinical importance of GAS2 family in HCC. In this study, we found that GAS2L1 and GAS2L3 were distinctly upregulated in HCC specimens compared to non-tumor specimens. Pan-cancer assays indicated that GAS2L1 and GAS2L3 were highly expressed in most cancers. The Pearson's correlation revealed that the expressions of GAS2, GAS2L1 and GAS2L2 were negatively associated with methylation levels. Survival assays indicated that GAS2L1 and GAS2L3 were independent prognostic factors for HCC patients. Immune cell infiltration analysis revealed that GAS2, GAS2L1 and GAS2L3 were associated with several immune cells. Finally, we confirmed that GAS2L1 was highly expressed in HCC cells and its knockdown suppressed the proliferation of HCC cells. Taken together, our findings suggested the expression patterns and prognostic values of GAS2 members in HCC, providing insights for further study of the GAS2 family as sensitive diagnostic and prognostic markers for HCC.

The role of autophagy regulated by the PI3K/AKT/mTOR pathway and innate lymphoid cells in eosinophilic chronic sinusitis with nasal polyps.

BACKGROUND: The PI3K/Akt/mTOR pathway and autophagy are important physiological processes. But their roles in eCRSwNP remains controversial. METHODS: In this study, we used the eCRSwNP mouse model, PI3K/Akt/mTOR pathway inhibitors, and autophagy inhibitors and activators to investigate the regulatory effects of the PI3K/Akt/mTOR pathway on autophagy, and their effects on eosinophilic inflammation, and tissue remodeling. The role of ILC2s in eCRSwNP was also studied, and the relationship between ILC2s and autophagy was preliminarily determined. RESULTS: Our results show that eosinophilic inflammation in eCRSwNP mice could be inhibited by promoting the autophagy; otherwise, eosinophilic inflammation could be promoted. Meanwhile, inhibition of the PI3K/Akt/mTOR pathway can further promote autophagy and inhibit eosinophilic inflammation. Meanwhile, inhibiting the PI3K/Akt/mTOR pathway and promoting autophagy can reduce the number of ILC2s and the severity of tissue remodeling in the nasal polyps of eCRSwNP mice. CONCLUSIONS: We conclude that the PI3K/Akt/mTOR pathway plays roles in eosinophilic inflammation and tissue remodeling of eCRSwNP, in part by regulating the level of autophagy. The downregulation of autophagy is a pathogenesis of eCRSwNP; therefore, the recovery of normal autophagy levels might be a new target for eCRSwNP therapy. Furthermore, autophagy might inhibit eosinophilic inflammation and tissue remodeling, in part by reducing the number of ILC2s.

Inflammatory microenvironment regulation and osteogenesis promotion by bone-targeting calcium and magnesium repletion nanoplatform for osteoporosis therapy.

Osteoporosis is the most common bone metabolic disease that affects the health of middle-aged and elderly people, which is hallmarked by imbalanced bone remodeling and a deteriorating immune microenvironment. Magnesium and calcium are pivotal matrix components that participate in the bone formation process, especially in the immune microenvironment regulation and bone remodeling stages. Nevertheless, how to potently deliver magnesium and calcium to bone tissue remains a challenge. Here, we have constructed a multifunctional nanoplatform composed of calcium-based upconversion nanoparticles and magnesium organic frameworks (CM-NH2-PAA-Ald, denoted as CMPA), which features bone-targeting and pH-responsive properties, effectively regulating the inflammatory microenvironment and promoting the coordination of osteogenic functions for treating osteoporosis. The nanoplatform can efficaciously target bone tissue and gradually degrade in response to the acidic microenvironment of osteoporosis to release magnesium and calcium ions. This study validates that CMPA possessing favorable biocompatibility can suppress inflammation and facilitate osteogenesis to treat osteoporosis. Importantly, high-throughput sequencing results demonstrate that the nanoplatform exerts a good inflammatory regulation effect through inhibition of the nuclear factor kappa-B signaling pathway, thereby normalizing the osteoporotic microenvironment. This collaborative therapeutic strategy that focuses on improving bone microenvironment and promoting osteogenesis provides new insight for the treatment of metabolic diseases such as osteoporosis.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics.

BACKGROUND: Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis. RESULTS: Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples. CONCLUSION: A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958-2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000-2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Atomic-Scale Distribution and Evolution of Strain in Pt Nanoparticles Grown on MoS2 Nanosheet.

Interface strain significantly affects the band structure and electronic states of metal-nanocrystal-2D-semiconductor heterostructures, impacting system performance. While transmission electron microscopy (TEM) is a powerful tool for studying interface strain, its accuracy may be compromised by sample overlap in high-resolution images due to the unique nature of the metal-nanocrystals-2D-semiconductors heterostructure. Utilizing digital dark-field technology, the substrate influence on metal atomic column contrasts is eliminated, improving the accuracy of quantitative analysis in high-resolution TEM images. Applying this method to investigate Pt on MoS2 surfaces reveals that the heterostructure introduces a tensile strain of ≈3% in Pt nanocrystal. The x-directional linear strain in Pt nanocrystals has a periodic distribution that matches the semi-coherent interface between Pt nanocrystals and MoS2, while the remaining strain components localize mainly on edge atomic steps. These results demonstrate an accurate and efficient method for studying interface strain and provide a theoretical foundation for precise heterostructure fabrication.

Circulating fatty acids and risk of hepatocellular carcinoma and chronic liver disease mortality in the UK Biobank.

Nuclear magnetic resonance (NMR)-based plasma fatty acids are objective biomarkers of many diseases. Herein, we aim to explore the associations of NMR-based plasma fatty acids with the risk of hepatocellular carcinoma (HCC) and chronic liver disease (CLD) mortality in 252,398 UK Biobank participants. Here we show plasma levels of n-3 poly-unsaturated fatty acids (PUFA) and n-6 PUFA are negatively associated with the risk of incident HCC [HRQ4vsQ1: 0.48 (95% CI: 0.33-0.69) and 0.48 (95% CI: 0.28-0.81), respectively] and CLD mortality [HRQ4vsQ1: 0.21 (95% CI: 0.13-0.33) and 0.15 (95% CI: 0.08-0.30), respectively], whereas plasma levels of saturated fatty acids are positively associated with these outcomes [HRQ4vsQ1: 3.55 (95% CI: 2.25-5.61) for HCC and 6.34 (95% CI: 3.68-10.92) for CLD mortality]. Furthermore, fibrosis stage significantly modifies the associations between PUFA and CLD mortality. This study contributes to the limited prospective evidence on the associations between plasma-specific fatty acids and end-stage liver outcomes.

Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation.

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash-Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

Association analyses between the variants of SNAP25, SV2C and ST3GAL2 and the efficacy of botulinum toxin A in the treatment of the primary Meige syndrome.

Objective: Individual differences were observed in the clinical efficacy of Botulinum toxin A (BoNT-A) in the treatment of the primary Meige syndrome. Our study aimed to explore the potential associations between the clinical efficacy of BoNT-A in the treatment of the primary Meige syndrome and variants of SNAP25, SV2C and ST3GAL2, which are involving in the translocation of the BoNT-A in vivo. Methods: Patients with the primary Meige syndrome treated with BoNT-A were enrolled. Clinical efficacy was evaluated by the maximum improvement rate of motor symptoms and the duration of efficacy. Variants of SNAP25, SV2C and ST3GAL2 were obtained by Sanger sequencing. Another cohort diagnosed with primary cervical dystonia was also enrolled in the replication stage. Results: Among the 104 primary Meige syndrome patients, 80 patients (76.9%) had a good efficacy (the maximum improvement rate of motor symptoms ≥30%) and 24 (23. 1%) had a poor (the maximum improvement rate of motor symptoms <30%). As to the duration of efficacy, 52 patients (50.0%) had a long duration of efficacy (≥4 months), and 52 (50.0%) had a short (<4 months). In terms of primary Meige syndrome, SNAP25 rs6104571 was found associating with the maximum improvement rate of motor symptoms (Genotype: P = 0.02, OR = 0.26; Allele: P = 0.013, OR = 0.29), and SV2C rs31244 was found associating with the duration of efficacy (Genotype: P = 0.024, OR = 0.13; Allele: P = 0.012, OR = 0.13). Besides, we also conducted the association analyses between the variants and BoNT-A-related adverse reactions. Although, there was no statistical difference between the allele of SV2C rs31244 and BoNT-A-related adverse reactions, there was a trend (P = 0.077, OR = 2.56). In the replication stage, we included 39 patients with primary cervical dystonia to further expanding the samples' size. Among the 39 primary cervical dystonia patients, 25 patients (64.1%) had a good efficacy (the maximum improvement rate of motor symptoms ≥50%) and 14 (35.9%) had a poor (the maximum improvement rate of motor symptoms <50%). As to the duration of efficacy, 32 patients (82.1%) had a long duration of efficacy (≥6 months), and 7 (17.9%) had a short (<6 months). Integrating primary Meige syndrome and primary cervical dystonia, SV2C rs31244 was still found associating with the duration of efficacy (Genotype: P = 0.002, OR = 0. 23; Allele: P = 0.001, OR = 0. 25). Conclusion: In our study, SNAP25 rs6104571 was associated with the maximum improvement rate of motor symptoms in patients with primary Meige syndrome treated with BoNT-A, and patients carrying this variant had a lower improvement rate of motor symptoms. SV2C rs31244 was associated with duration of treatment in patients with primary Meige syndrome treated with BoNT-A and patients carrying this variant had a shorter duration of treatment. Patients with primary Meige syndrome carrying SV2C rs31244 G allele have an increase likelihood of BoNT-A-related adverse reactions. Involving 39 patients with primary cervical dystonia, the results further verify that SV2C rs31244 was associated with duration of treatment and patients carrying this variant had a shorter duration of treatment.

Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues.

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.