[Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine].

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

[A new cucurbitane glycoside from Picrorhiza scrophulariiflora].

Chemical constituents from the ethanol extract of Picrorhiza scrophulariiflora were isolated and purified by column chromatography. Their structures were identified by HR-MS, 1D and 2D-NMR, and their cytotoxicity was assessed by CCK-8 assay. Four compounds were isolated and identified as follows: 2ß-D-glucosyloxy-3ß,16α,20ß-trihydroxy-9-methyl-19-norlanosterol-5,25-diene-22-one(1), 2ß-D-glucosyloxy-3ß,16α,20ß-trihydroxy-9-methyl-19-norlanosta-5,24-diene-22-one(2), 25-acetoxy-2ß-glucosyloxy-3ß,16α,20ß-trihydroxy-9-methyl-19-norlanosta-5-ene-22-one(3) and 25-acetoxy-2ß-glucosyloxy-3ß,16α,20ß-trihydroxy-9-methyl-19-norlanosta-5,23-(E)-diene-22-one(4). Compound 1 represents a new cucurbitane glycoside. The half inhibitory concentrations of the 4 compounds exceeded 100 µmol·L~(-1) against four tumor cell lines, indicating no significant cytotoxicity.

Common alterations in parallel metabolomic profiling of serum and spinal cord and mechanistic studies on neuropathic pain following PPARα administration.

Neuropathic pain (NP) is usually treated with analgesics and symptomatic therapy with poor efficacy and numerous side effects, highlighting the urgent need for effective treatment strategies. Recent studies have reported an important role for peroxisome proliferator-activated receptor alpha (PPARα) in regulating metabolism as well as inflammatory responses. Through pain behavioral assessment, we found that activation of PPARα prevented chronic constriction injury (CCI)-induced mechanical allodynia and thermal hyperalgesia. In addition, PPARα ameliorated inflammatory cell infiltration at the injury site and decreased microglial activation, NOD-like receptor protein 3 (NLRP3) inflammasome production, and spinal dendritic spine density, as well as improved serum and spinal cord metabolic levels in mice. Administration of PPARα antagonists eliminates the analgesic effect of PPARα agonists. PPARα relieves NP by inhibiting neuroinflammation and functional synaptic plasticity as well as modulating metabolic mechanisms, suggesting that PPARα may be a potential molecular target for NP alleviation. However, the effects of PPARα on neuroinflammation and synaptic plasticity should be further explored.

Deoxynivalenol and T-2 toxin cause liver damage and egg quality degradation through endoplasmic reticulum stress in summer laying hens.

The present study aimed to find whether low doses of mixed mycotoxins would affect egg quality in laying hens, and to explore the oxidative stress induced liver damage through endoplasmic reticulum during summer stress. A total of 96 Jinghong laying hens, 36 wks of age, were divided into four treatments, with eight repetitions per treatment and three hens per repetition. All the hens were raised in summer (average temperature: 31.3 ± 0.5℃; average humidity: 85.5 ± 0.2%) for 28d. One treatment was fed a basal diet as control (CON), and the other three treatments were fed the same diets containing 3.0 mg/kg deoxynivalenol (DON), 0.5 mg/kg T-2 toxin (T-2), and 1.5 mg/kg DON + 0.25 mg/kg T-2 toxin (Mix). Albumen height and Haugh unit were decreased (P < 0.05) in the Mix group on day 14 and 28. The activity of total antioxidant capacity, glutathione peroxidase, catalase, and superoxide dismutase were decreased (P < 0.05) in the DON, T-2, and Mix groups. The alkaline phosphatase level in DON, T-2, and Mix groups was significantly increased (P < 0.05). The level of interleukin-1ß, interferon-γ, and tumor necrosis factor-α in the Mix group were higher (P < 0.05) than CON, DON, and T-2 groups. Mix group upregulated the mRNA expressions of protein kinase RNA-like ER kinase, activating transcription factor4, IL-1ß, nuclear factor-κ-gene binding, and nuclear respiratory factor 2 in the liver (P < 0.05). The results showed that low doses of DON and T-2 toxin could cause oxidative stress in the liver, but DON and T-2 toxin have a cumulative effect on virulence, which can reduce egg quality and cause endoplasmic reticulum stress in the liver.

Combining fecal 16 S rRNA sequencing and spinal cord metabolomics analysis to explain the modulatory effect of PPARα on neuropathic pain.

BACKGROUND: Existing evidence suggests that the composition of the gut microbiota is associated with neuropathic pain (NP), but the mechanistic link is elusive. Peroxisome proliferator-activated receptor α (PPARα) has been shown to be a pharmacological target for the treatment of metabolic disorders, and its expression is also involved in inflammatory regulation. The aim of this study was to investigate the important modulatory effects of PPARα on gut microbiota and spinal cord metabolites in mice subjected to chronic constriction injury. METHODS: We analyzed fecal microbiota and spinal cord metabolic alterations in mice from the sham, CCI, GW7647 (PPARα agonist) and GW6471 (PPARα antagonist) groups by 16 S rRNA amplicon sequencing and untargeted metabolomics analysis. On this basis, the intestinal microbiota and metabolites that were significantly altered between treatment groups were analyzed in a combined multiomics analysis. We also investigated the effect of PPARα on the polarization fractionation of spinal microglia. RESULTS: PPARα agonist significantly reduce paw withdrawal threshold and paw withdrawal thermal latency, while PPARα antagonist significantly increase paw withdrawal threshold and paw withdrawal thermal latency. 16 S rRNA gene sequencing showed that intraperitoneal injection of GW7647 or GW6471 significantly altered the abundance, homogeneity and composition of the gut microbiome. Analysis of the spinal cord metabolome showed that the levels of spinal cord metabolites were shifted after exposure to GW7647 or GW6471. Alterations in the composition of gut microbiota were significantly associated with the abundance of various spinal cord metabolites. The abundance of Licheniformes showed a significant positive correlation with nicotinamide, benzimidazole, eicosanoids, and pyridine abundance. Immunofluorescence results showed that intraperitoneal injection of GW7647 or GW6471 altered microglial activation and polarization levels. CONCLUSION: Our study shows that PPARα can promote M2-type microglia polarization, as well as alter gut microbiota and metabolites in CCI mice. This study enhances our understanding of the mechanism of PPARα in the treatment of neuropathic pain.

Bosea beijingensis sp. nov., Telluria beijingensis sp. nov. and Agrococcus beijingensis sp. nov., isolated from baijiu mash.

The baijiu fermentation environment hosts a variety of micro-organisms, some of which still remain uncultured and uncharacterized. In this study, the isolation, cultivation and characterization of three novel aerobic bacterial strains are described. The cells of strain REN20T were Gram-negative, strictly aerobic, motile and grew at 26-37 °C, at pH 6.0-9.0 and in the presence of 0-5.0   % (w/v) NaCl. The cells of strain REN29T were Gram-negative, strictly aerobic, motile and grew at 15-30 °C, at pH 6.0-9.0 and in the presence of 0-10.0   % (w/v) NaCl. The cells of strain REN33T were Gram-positive, strictly aerobic, motile and grew at 15-37 °C, at pH 5.0-10.0 and in the presence of 0-7.0   % (w/v) NaCl. The digital DNA-DNA hybridization and average nucleotide identity by orthology values between type strains in related genera and REN20T (20.3-36.8 % and 79.8-89.9  %), REN29T (20.3-36.8  % and 74.5-88.5  %) and REN33T (22.6-48.6  % and 75.8-84.2  %) were below the standard cut-off criteria for the delineation of bacterial species, respectively. Based on polyphasic taxonomy analysis, we propose three new species, Bosea beijingensis sp. nov. (=REN20T=GDMCC 1.2894T=JCM 35118T), Telluria beijingensis sp. nov. (=REN29T=GDMCC 1.2896T=JCM 35119T) and Agrococcus beijingensis sp. nov. (=REN33T=GDMCC 1.2898T=JCM 35164T), which were recovered during cultivation and isolation from baijiu mash.

Alleviating sleep disturbances and modulating neuronal activity after ischemia: Evidence for the benefits of zolpidem in stroke recovery.

AIMS: Sleep disorders are prevalent among stroke survivors and impede stroke recovery, yet they are still insufficiently considered in the management of stroke patients, and the mechanisms by which they occur remain unclear. There is evidence that boosting phasic GABA signaling with zolpidem during the repair phase improves stroke recovery by enhancing neural plasticity; however, as a non-benzodiazepine hypnotic, the effects of zolpidem on post-stroke sleep disorders remain unclear. METHOD: Transient ischemic stroke in male rats was induced with a 30-minute middle cerebral artery occlusion. Zolpidem or vehicle was intraperitoneally delivered once daily from 2 to 7 days after the stroke, and the electroencephalogram and electromyogram were recorded simultaneously. At 24 h after ischemia, c-Fos immunostaining was used to assess the effect of transient ischemic stroke and acute zolpidem treatment on neuronal activity. RESULTS: In addition to the effects on reducing brain damage and mitigating behavioral deficits, repeated zolpidem treatment during the subacute phase of stroke quickly ameliorated circadian rhythm disruption, alleviated sleep fragmentation, and increased sleep depth in ischemic rats. Immunohistochemical staining showed that in contrast to robust activation in para-infarct and some remote areas by 24 h after the onset of focal ischemia, the activity of the ipsilateral suprachiasmatic nucleus, the biological rhythm center, was strongly suppressed. A single dose of zolpidem significantly upregulated c-Fos expression in the ipsilateral suprachiasmatic nucleus to levels comparable to the contralateral side. CONCLUSION: Stroke leads to suprachiasmatic nucleus dysfunction. Zolpidem restores suprachiasmatic nucleus activity and effectively alleviates post-stroke sleep disturbances, indicating its potential to promote stroke recovery.

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma.

BACKGROUND & AIMS: The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model. METHODS: We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison. RESULTS: Seven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973-0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835- 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively). CONCLUSION: CT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

Arthroscopic long head of the biceps tendon transposition for augmented repair of massive rotator cuff tear / 中国组织工程研究

BACKGROUND:Transposition of the long head of biceps tendon is a commonly surgical method for massive rotator cuff tears.Currently,there are a few reports on the clinical efficacy of the transposition of the long head of biceps tendon and there is no consensus on the influencing factors for retearing. OBJECTIVE:To observe the outcome of arthroscopic long head of the biceps tendon in the treatment of massive rotator cuff tear. METHODS:The clinical data of 28 patients with massive rotator cuff tears,aged(61.79±10.50)years,admitted at Jiangsu Province Hospital of Chinese Medicine from March 2019 to May 2022 were retrospectively analyzed.All patients underwent arthroscopic long head of the biceps tendon.Patients were assessed for visual analog scale scores,University of California at Los Angeles scores,American Shoulder and Elbow Surgeons scores,Constant-Murley scores,and shoulder range of motion before and 1 year after operation.MRI of the shoulder joint was performed for observing the integrity of the repaired structure at 1 year after operation.Twenty-three patients(5 of 28 lost to follow-up)were categorized into the intact tendon group(n=18)and the tendon retear group(n=5)according to the Sugaya typing at 1 year after operation;the patients were divided into the normal group(n=8),the degeneration group(n=9),and the partial tear group(n=6)according to the intraoperative quality of the long head of the biceps tendon.Differences in the above indexes were compared between groups. RESULTS AND CONCLUSION:When followed up at 1 year after surgery,the range of motion,visual analog scale scores,University of California at Los Angeles scores,American Shoulder and Elbow Surgeons scores,Constant-Murley scores of the shoulder were significantly improved compared with preoperative data(P＜0.05).There was a significant difference in Goutellier grading between intact tendon and tendon retear groups(P＜0.05),while no significant difference was observed in the other influencing factors(P＞0.05).There were no significant differences in visual analog scale scores,University of California at Los Angeles scores,American Shoulder and Elbow Surgeons scores,Constant-Murley scores,and shoulder range of motion at 1 year after operation among the normal,degeneration,and partial tear groups(P＞0.05).MRI findings indicated that the sutured tendon healed well in 18 patients,with a healing rate of 78%.Arthroscopic long head of the biceps tendon for augmented repair can provide a reliable repair for massive rotator cuff tear that is refractory,significantly alleviate the pain of the shoulder joint,and restore the function of the shoulder joint.

Analysis of risk factors and their warning effectiveness for meniscus tear secondary to delayed anterior cruciate ligament reconstruction / 中华创伤杂志

Objective:To investigate the risk factors and their warning effectiveness for meniscus tear secondary to delayed anterior cruciate ligament (ACL) reconstruction.Methods:A retrospective cohort study was conducted to analyze the clinical data of 114 patients (114 knees) with ACL injury, who were admitted to Affiliated Hospital of Nanjing University of Chinese Medicine from December 2018 to December 2023, including 78 males and 36 females, aged 11-50 years [29(21, 35)years]. The patients were divided into tear group ( n=46) and non-tear group ( n=68) according to whether combined with meniscus tear or not during the surgery. Gender, age, duration of disease, smoking history, Body Mass Index (BMI), cause of injury, mechanism of injury, side of the injury, Beighton score, difference of KT-2000 examination, Tegner activity scale of the knee joint, anterior tibial translation sign, medial posterior tibial slope angle, lateral posterior tibial slope angle, and pivot shift grading of the patients in the two groups were recorded. Correlations between the above-mentioned indicators and occurrence of meniscus tear secondary to delayed ACL reconstruction were assessed. In the meantime, the independent risk factors were determined by univariate and multivariate binary logistic regression analysis. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to evaluate the warning effectiveness of each risk factor for meniscal tear secondary to delayed ACL reconstruction. Results:Univariate analysis showed correlation of duration of disease, BMI, Tegner activity scale of the knee joint, medial posterior tibial slope angle, lateral posterior tibial slope angle and high-grade pivot shift with occurrence of meniscus tear secondary to delayed ACL reconstruction ( P<0.01). The results of multivariate binary logistic regression analysis showed that the duration of disease ≥14.5 weeks ( OR=1.20, 95% CI 1.05, 1.38, P<0.01), BMI≥26.9 kg/m 2 ( OR=1.36, 95% CI 1.03, 1.81, P<0.05), Tegner activity scale of the knee joint ≥4 points ( OR=2.29, 95% CI 1.18, 4.46, P<0.05), medial posterior tibial slope angle ≥11.2° ( OR=2.27, 95% CI 1.06, 4.89, P<0.05) and high-grade pivot shift ( OR=0.03, 95% CI 0.01, 0.03, P<0.05) were significantly correlated with occurrence of meniscus tear secondary to delayed ACL reconstruction. Results of ROC curve analysis showed that the medial posterior tibial slope angle (AUC=0.86, 95% CI 0.80, 0.93) and duration of disease (AUC=0.85, 95% CI 0.77, 0.92) had good warning value, BMI (AUC=0.78, 95% CI 0.69, 0.87) and Tegner activity scale of the knee joint (AUC=0.73, 95% CI 0.64, 0.83) had ordinary warning value, and the warning value of high-grade pivot shift (AUC=0.60, 95% CI 0.49, 0.71) was the lowest. The combination of the risk factors revealed superior warning effectiveness for meniscus tear second to delayed ACL reconstruction (AUC=0.97, 95% CI 0.96, 1.00). Conclusions:The duration of disease ≥14.5 weeks, BMI ≥26.9 kg/m 2, Tegner score ≥4 points, medial posterior tibial slope angle ≥11.2°and high-grade pivot shift are independent risk factors for meniscus tear secondary to delayed ACL reconstruction. The medial posterior tibial slope angle and duration of disease have good warning value, BMI and Tegner scores have ordinary warning value and high-grade pivot shift has the lowest warning value. The combination of the above risk factors has better warning effectiveness for meniscus tear secondary to delayed ACL reconstruction.

Loss of LBP triggers lipid metabolic disorder through H3K27 acetylation-mediated C/EBPß- SCD activation in non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is associated with mutations in lipopolysaccharide-binding protein ( LBP), but the underlying epigenetic mechanisms remain understudied. Herein, LBP -/- rats with NAFLD were established and used to conduct integrative targeting-active enhancer histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation coupled with high-throughput and transcriptomic sequencing analysis to explore the potential epigenetic pathomechanisms of active enhancers of NAFLD exacerbation upon LBP deficiency. Notably, LBP -/- reduced the inflammatory response but markedly aggravated high-fat diet (HFD)-induced NAFLD in rats, with pronounced alterations in the histone acetylome and regulatory transcriptome. In total, 1 128 differential enhancer-target genes significantly enriched in cholesterol and fatty acid metabolism were identified between wild-type (WT) and LBP -/- NAFLD rats. Based on integrative analysis, CCAAT/enhancer-binding protein ß (C/EBPß) was identified as a pivotal transcription factor (TF) and contributor to dysregulated histone acetylome H3K27ac, and the lipid metabolism gene SCD was identified as a downstream effector exacerbating NAFLD. This study not only broadens our understanding of the essential role of LBP in the pathogenesis of NAFLD from an epigenetics perspective but also identifies key TF C/EBPß and functional gene SCD as potential regulators and therapeutic targets.

[Application progress on data-driven technologies in intelligent manufacturing of traditional Chinese medicine extraction].

The application of new-generation information technologies such as big data, the internet of things(IoT), and cloud computing in the traditional Chinese medicine(TCM)manufacturing industry is gradually deepening, driving the intelligent transformation and upgrading of the TCM industry. At the current stage, there are challenges in understanding the extraction process and its mechanisms in TCM. Online detection technology faces difficulties in making breakthroughs, and data throughout the entire production process is scattered, lacking valuable mining and utilization, which significantly hinders the intelligent upgrading of the TCM industry. Applying data-driven technologies in the process of TCM extraction can enhance the understanding of the extraction process, achieve precise control, and effectively improve the quality of TCM products. This article analyzed the technological bottlenecks in the production process of TCM extraction, summarized commonly used data-driven algorithms in the research and production control of extraction processes, and reviewed the progress in the application of data-driven technologies in the following five aspects: mechanism analysis of the extraction process, process development and optimization, online detection, process control, and production management. This article is expected to provide references for optimizing the extraction process and intelligent production of TCM.

Thermal, photonic, and electrocatalysis in lignin depolymerization research.

In order to realize a sustainable bio-based future, it is essential to fully harness the potential of biomass, including lignin - a readily available biopolymer that ranks second in abundance and serves as a renewable source of aromatics. While lignin has traditionally been used for lower-value applications like fuel and power generation, unlocking its higher-value potential through diverse conversion and upgrading techniques is of paramount importance. This review focuses on the catalytic conversion of lignin, with a specific emphasis on selective depolymerization, a process that not only supports economically and environmentally sustainable biorefineries but also aligns with Green Chemistry principles, mitigating adverse environmental impacts. Furthermore, we provide a comprehensive discussion of reaction pathways and mechanisms, including C-O and C-C bond cleavage, among different catalysts. Lastly, we analyze and briefly discuss the prospects of rational catalyst design in biomass valorization.

HAKE: A Knowledge Engine Foundation for Human Activity Understanding.

Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis. Although there have been advances with deep learning, it remains challenging. The object recognition-like solutions usually try to map pixels to semantics directly, but activity patterns are much different from object patterns, thus hindering another success. In this article, we propose a novel paradigm to reformulate this task in two-stage: first mapping pixels to an intermediate space spanned by atomic activity primitives, then programming detected primitives with interpretable logic rules to infer semantics. To afford a representative primitive space, we build a knowledge base including 26+ M primitive labels and logic rules from human priors or automatic discovering. Our framework, Human Activity Knowledge Engine (HAKE), exhibits superior generalization ability and performance upon canonical methods on challenging benchmarks. Code and data are available at http://hake-mvig.cn/.

Efficiencies of Various in situ Polymerizations of Liquid Electrolytes and the Practical Implications for Quasi Solid-state Batteries.

In situ polymerization of liquid electrolytes is currently the most feasible way for constructing solid-state batteries, which, however, is affected by various interfering factors of reactions and so the electrochemical performance of cells. To disclose the effects from polymerization conditions, two types of generally used in situ polymerizing reactions of ring-opening polymerization (ROP) and double bond radical polymerization (DBRP) were investigated on the aspects of monomer conversion and electrochemical properties (Li+ -conductivity and interfacial stability). The ROP generated poly-ester and poly-carbonate show a high monomer conversion of ≈90 %, but suffer a poor Li+ -conductivity of lower than 2×10-5  S cm-1 at room temperature (RT). Additionally, the terminal alkoxy anion derived from the ROP is not resistant to high-voltage cathodes. While, the DBRP produced poly-VEC(vinyl ethylene carbonate) and poly-VC(vinylene carbonate) show lower monomer conversions of 50-80 %, delivering relatively higher Li+ -conductivities of 2×10-4  S cm-1 at RT. Compared two polymerizing reactions and four monomers, the VEC-based F-containing copolymer possesses advantages in Li+ -conductivity and antioxidant capacity, which also shows simultaneous stability towards Li-metal with the help of LiF-based passivating layer, allowing a long-term stable cycling of high-voltage quasi solid-state cells.

AlphaTracker: a multi-animal tracking and behavioral analysis tool.

Computer vision has emerged as a powerful tool to elevate behavioral research. This protocol describes a computer vision machine learning pipeline called AlphaTracker, which has minimal hardware requirements and produces reliable tracking of multiple unmarked animals, as well as behavioral clustering. AlphaTracker pairs a top-down pose-estimation software combined with unsupervised clustering to facilitate behavioral motif discovery that will accelerate behavioral research. All steps of the protocol are provided as open-source software with graphic user interfaces or implementable with command-line prompts. Users with a graphical processing unit (GPU) can model and analyze animal behaviors of interest in less than a day. AlphaTracker greatly facilitates the analysis of the mechanism of individual/social behavior and group dynamics.

The inhibition of Aurora A kinase regulates phospholipid remodeling by upregulating LPCAT1 in glioblastoma.

Metabolic reprogramming is a common feature of glioblastoma (GBM) progression and metastasis. Altered lipid metabolism is one of the most prominent metabolic alterations in cancer. Understanding the links between phospholipid remodeling and GBM tumorigenesis may help develop new anticancer strategies and improve treatments to overcome drug resistance. We used metabolomic and transcriptomic analyses to systematically investigate metabolic and molecular changes in low-grade glioma (LGG) and GBM. We then re-established the reprogrammed metabolic flux and membrane lipid composition in GBM based on metabolomic and transcriptomic analyses. By inhibiting Aurora A kinase via RNA interference (RNAi) and inhibitor treatment, we investigated the effect of Aurora A kinase on phospholipid reprogramming LPCAT1 enzyme expression and GBM cell proliferation in vitro and in vivo. We found that GBM displayed aberrant glycerophospholipid and glycerolipid metabolism compared with LGG. Metabolic profiling indicated that fatty acid synthesis and uptake for phospholipid synthesis were significantly increased in GBM compared to LGG. The unsaturated phosphatidylcholine (PC) and phosphatidylethanolamine (PE) levels were significantly decreased in GBM compared to LGG. The expression level of LPCAT1, which is required for the synthesis of saturated PC and PE, was upregulated in GBM, and the expression of LPCAT4, which is required for the synthesis of unsaturated PC and PE, was downregulated in GBM. Notably, the inhibition of Aurora A kinase by shRNA knockdown and treatment with Aurora A kinase inhibitors such as Alisertib, AMG900, or AT9283 upregulated LPCAT1 mRNA and protein expression in vitro. In vivo, the inhibition of Aurora A kinase with Alisertib increased LPCAT1 protein expression. Phospholipid remodeling and a reduction in unsaturated membrane lipid components were found in GBM. Aurora A kinase inhibition increased LPCAT1 expression and suppressed GBM cell proliferation. The combination of Aurora kinase inhibition with LPCAT1 inhibition may exert promising synergistic effects on GBM.

AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time.

Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this article, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose.