Volatile oil from Acori graminei Rhizoma affected the synaptic plasticity of rats with tic disorders by modulating dopaminergic and glutamatergic systems.

ETHNOPHARMACOLOGICAL RELEVANCE: Acori graminei Rhizoma is a commonly used traditional Chinese medicine for treating TD, with its main component being calamus volatile oil. Volatile Oil from Acori graminei Rhizoma (VOA)can protect nerve cells and alleviate learning and memory disorders. However, the mechanism of anti-tic of VOA is still unclear. AIM OF THE STUDY: We aimed to explore the effects of Volatile Oil from Acori Tatarinowii Rhizoma (VOA) on striatal dopaminergic and glutamatergic systems and synaptic plasticity of rats with Tic Disorder (TD), as well as its pharmaceutical mechanism against TD. MATERIALS AND METHODS: This study involved 48 (three-week-old) Sprague Dawley (SD) rats, which were randomly divided into two primary groups: Control (8) and TD (40). Rats in the TD group were injected intraperitoneally with 3,3-iminodipropionitrile (IDPN) to construct the TD rat model. They were divided into five subgroups: Model, Tiapride, VOA-high, VOA-medium, and VOA-low (N = 8). After modeling, VOA was administrated to rats in the VOA groups through gavage (once/day for four consecutive weeks), while rats in the blank control and model groups received normal saline of the same volume. The animals' behavioral changes were reflected using the stereotypic and motor behavior scores. After interferences, patterns of striatal neurons and the density of dendritic spines were investigated using H&E and Golgi staining, and the ultrastructure of striatal synapses was examined using Transmission Electron Microscopy (TEM). Furthermore, Ca2+ content was determined using the Ca2+ detector, and Dopamine (DA) and Glutamate (GLU) contents in serum and striatum were detected through ELISA. Finally, DRD1, DRD2, AMPAR1, NMPAR1, DAT, VMAT2, CAMKⅡ, and CREB expression in the striatum was detected using Quantitative real-time PCR (qRT-PCR), Western Blotting (WB) and Immunohistochemical (IHC) methods. RESULTS: Compared to rats in the blank control and model groups, rats in the VOA groups showed lower stereotypic behavior scores. Furthermore, rats in the VOA groups exhibited relieved, neuron damage and increased quantities of neuronal dendrites and dendritic spines Additionally, based on TEM images show that, the VOA groups showed a clear synaptic structure and increased amounts of postsynaptic dense substances and synaptic vesicles. The VOA groups also exhibited reduced Ca2+ contents, and upregulation of DRD1, DRD2, DAT, AMPAR1, and NMPAR1 and downregulation of VMAT-2, CAMKⅡ, and CREB in the striatum. CONCLUSIONS: In summary, VOA could influence synaptic plasticity by tuning the dopaminergic and glutamatergic systems, thus relieving TD.

Association of M2 macrophages with EMT in glioma identified through combination of multi-omics and machine learning.

Background: The incidence of glioma, a prevalent brain malignancy, is increasing, particularly among the elderly population. This study aimed to elucidate the clinical importance of epithelial-mesenchymal transition (EMT) in gliomas and its association with malignancy and prognosis. Background: The incidence of glioma, particularly among elderly individuals, is on the rise. The malignancy of glioma is determined not only by the oncogenic properties of tumor cells but also by the composition of the tumor microenvironment, which includes immune system macrophages. The prevalence of M2-type macrophages typically fosters tumor progression, yet the underlying mechanism remains elusive. Our study explored the clinical importance of epithelial-mesenchymal transition (EMT) in gliomas and its association with malignancy and prognosis. Methods: Our study used the gene set variation analysis (GSVA) algorithm to classify different levels of EMT activation based on the transcriptomic and multi-omics data. Machine learning (ML) and single-cell analysis were integrated into our model for comprehensive analysis. A predictive model was constructed and in vitro experiments were performed to validate our findings. Results: Our study classified 1,641 samples into two clusters based on EMT activation: the EMT-hot group and the EMT-cold group. The EMT-hot group had elevated copy number loss, tumor mutational burden (TMB), and a poorer survival rate. Conversely, the EMT-cold group showed a better survival rate, likely attributed to lower stromal and immune cell scores, as well as decreased expression of human leukocyte antigen-related genes. Driving genes were identified through weighted gene coexpression network analysis (WGCNA) and dimensionality reduction techniques. These genes were then utilized in the construction of a prognostic model using ML and protein-protein interaction (PPI) network analysis. Furthermore, the impact of the core genes identified through single-cell analysis on glioma prognosis was examined. Conclusion: Our research underscores the efficacy of our model in predicting glioma prognosis and elucidates the connection between the M2 macrophages and EMT. Additionally, core genes such as LY96, C1QB, LGALS1, CSPG5, S100A8, and CHGB were identified as pivotal for mediating the occurrence of EMT induced by M2 macrophages.

LA-ViT: A Network With Transformers Constrained by Learned-Parameter-Free Attention for Interpretable Grading in a New Laryngeal Histopathology Image Dataset.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

Development of an online calculator for the prediction of seizure freedom following pediatric hemispherectomy using the Hemispherectomy Outcome Prediction Scale (HOPS).

OBJECTIVES: Although hemispheric surgeries are among the most effective procedures for drug-resistant epilepsy (DRE) in the pediatric population, there is a large variability in seizure outcomes at the group level. A recently developed HOPS score provides individualized estimation of likelihood of seizure freedom to complement clinical judgement. The objective of this study was to develop a freely accessible online calculator that accurately predicts the probability of seizure freedom for any patient at 1-, 2-, and 5-years post-hemispherectomy. METHODS: Retrospective data of all pediatric patients with DRE and seizure outcome data from the original Hemispherectomy Outcome Prediction Scale (HOPS) study were included. The primary outcome of interest was time-to-seizure recurrence. A multivariate Cox proportional-hazards regression model was developed to predict the likelihood of post-hemispheric surgery seizure freedom at three time points (1-, 2- and 5- years) based on a combination of variables identified by clinical judgment and inferential statistics predictive of the primary outcome. The final model from this study was encoded in a publicly accessible online calculator on the International Network for Epilepsy Surgery and Treatment (iNEST) website (https://hops-calculator.com/). RESULTS: The selected variables for inclusion in the final model included the five original HOPS variables (age at seizure onset, etiologic substrate, seizure semiology, prior non-hemispheric resective surgery, and contralateral fluorodeoxyglucose-positron emission tomography [FDG-PET] hypometabolism) and three additional variables (age at surgery, history of infantile spasms, and magnetic resonance imaging [MRI] lesion). Predictors of shorter time-to-seizure recurrence included younger age at seizure onset, prior resective surgery, generalized seizure semiology, FDG-PET hypometabolism contralateral to the side of surgery, contralateral MRI lesion, non-lesional MRI, non-stroke etiologies, and a history of infantile spasms. The area under the curve (AUC) of the final model was 73.0%. SIGNIFICANCE: Online calculators are useful, cost-free tools that can assist physicians in risk estimation and inform joint decision-making processes with patients and families, potentially leading to greater satisfaction. Although the HOPS data was validated in the original analysis, the authors encourage external validation of this new calculator.

Research Progress Related to Aflatoxin Contamination and Prevention and Control of Soils.

Aflatoxins are potent carcinogenic compounds, mainly produced by fungi species of the genus Aspergillus in the soil. Because of their stability, they are difficult to remove completely, even under extreme conditions. Aflatoxin contamination is one of the main causes of safety in peanuts, maize, wheat and other agricultural products. Aflatoxin contamination originates from the soil. Through the investigation of soil properties and soil microbial distribution, the sources of aflatoxin are identified, aflatoxin contamination is classified and analysed, and post-harvest crop detoxification and corresponding contamination prevention measures are identified. This includes the team's recent development of the biofungicide ARC-BBBE (Aflatoxin Rhizobia Couple-B. amyloliquefaciens, B. laterosporu, B. mucilaginosus, E. ludwiggi) for field application and nanomaterials for post-production detoxification of cereals and oilseed crops, providing an effective and feasible approach for the prevention and control of aflatoxin contamination. Finally, it is hoped that effective preventive and control measures can be applied to a large number of cereal and oilseed crops.

Diversity-oriented synthesis of diterpenoid alkaloids yields a potent anti-inflammatory agent.

BACKGROUND: The diterpenoid alkaloids belong to a highly esteemed group of natural compounds, which display significant biological activities. It is a productive strategy to expand the chemical space of these intriguing natural compounds for drug discovery. METHODS: We prepared a series of new derivatives bearing diverse skeletons and functionalities from the diterpenoid alkaloids deltaline and talatisamine based on a diversity-oriented synthesis strategy. The anti-inflammatory activity of these derivatives was initially screened and evaluated by the release of nitric oxide (NO), tumor necrosis factor (TNF-α), and interleukin-6 (IL-6) in lipopolysaccharide (LPS)-activated RAW264.7 cells. Futhermore, the anti-inflammatory activity of the representative derivative 31a was validated in various inflammatory animal models, including phorbol ester 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced mice ear edema, LPS-stimulated acute kidney injury, and collagen-induced arthritis (CIA). RESULTS: It was found that several derivatives were able to suppress the secretion of NO, TNF-α, and IL-6 in LPS-activated RAW264.7 cells. Compound 31a, one of the representative derivatives named as deltanaline, demonstrated the strongest anti-inflammatory effects in LPS-activated macrophages and three different animal models of inflammatory diseases by inhibiting nuclear factor kappa-B (NF-κB)/mitogen-activated protein kinase (MAPK) signaling and inducing autophagy. CONCLUSION: Deltanaline is a new structural compound derived from natural diterpenoid alkaloids, which may serve as a new lead compound for the treatment of inflammatory diseases.

Immune exposure: how macrophages interact with the nucleus pulposus.

Intervertebral disc degeneration (IDD) is a primary contributor to low back pain. Immune cells play an extremely important role in modulating the progression of IDD by interacting with disc nucleus pulposus (NP) cells and extracellular matrix (ECM). Encased within the annulus fibrosus, healthy NP is an avascular and immune-privileged tissue that does not normally interact with macrophages. However, under pathological conditions in which neovascularization is established in the damaged disc, NP establishes extensive crosstalk with macrophages, leading to different outcomes depending on the different microenvironmental stimuli. M1 macrophages are a class of immune cells that are predominantly pro-inflammatory and promote inflammation and ECM degradation in the NP, creating a vicious cycle of matrix catabolism that drives IDD. In contrast, NP cells interacting with M2 macrophages promote disc tissue ECM remodeling and repair as M2 macrophages are primarily involved in anti-inflammatory cellular responses. Hence, depending on the crosstalk between NP and the type of immune cells (M1 vs. M2), the overall effects on IDD could be detrimental or regenerative. Drug or surgical treatment of IDD can modulate this crosstalk and hence the different treatment outcomes. This review comprehensively summarizes the interaction between macrophages and NP, aiming to highlight the important role of immunology in disc degeneration.

The Swin-Transformer network based on focal loss is used to identify images of pathological subtypes of lung adenocarcinoma with high similarity and class imbalance.

PURPOSE: The classification of primary lung adenocarcinoma is complex and varied. Different subtypes of lung adenocarcinoma have different treatment methods and different prognosis. In this study, we collected 11 datasets comprising subtypes of lung cancer and proposed FL-STNet model to provide the assistance for improving clinical problems of pathologic classification in primary adenocarcinoma of lung. METHODS: Samples were collected from 360 patients diagnosed with lung adenocarcinoma and other subtypes of lung diseases. In addition, an auxiliary diagnosis algorithm based on Swin-Transformer, which used Focal Loss for function in training, was developed. Meanwhile, the diagnostic accuracy of the Swin-Transformer was compared to pathologists. RESULTS: The Swin-Transformer captures not only information in the overall tissue structure but also the local tissue details in the images of lung cancer pathology. Furthermore, training FL-STNet with the Focal Loss function can further balance the difference in the amount of data between different subtypes, improving recognition accuracy. The average classification accuracy, F1, and AUC of the proposed FL-STNet reached 85.71%, 86.57%, and 0.9903. The average accuracy of the FL-STNet was higher by 17% and 34%, respectively, than in the senior pathologist and junior pathologist group. CONCLUSION: The first deep learning based on an 11-category classifier was developed for classifying lung adenocarcinoma subtypes based on WSI histopathology. Aiming at the deficiencies of the current CNN and Vit, FL-STNet model is proposed in this study by introducing Focal Loss and combining the advantages of Swin-Transformer model.

Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided attention.

Tumor grading and interpretability of laryngeal cancer is a key yet challenging task in the clinical diagnosis, mainly because of the commonly used low-magnification pathological images lack fine cellular structure information and accurate localization, the diagnosis results of pathologists are different from those of attentional convolutional network -based methods, and the gradient-weighted class activation mapping method cannot be optimized to create the best visualization map. To address this problem, we propose an end-to-end depth domain adaptive network (DDANet) with integration gradient CAM and priori experience-guided attention to improve the tumor grading performance and interpretability by introducing the pathologist's a priori experience in high-magnification into the depth model. Specifically, a novel priori experience-guided attention (PE-GA) method is developed to solve the traditional unsupervised attention optimization problem. Besides, a novel integration gradient CAM is proposed to mitigate overfitting, information redundancies and low sparsity of the Grad-CAM graphs generated by the PE-GA method. Furthermore, we establish a set of quantitative evaluation metric systems for model visual interpretation. Extensive experimental results show that compared with the state-of-the-art methods, the average grading accuracy is increased to 88.43% (↑4.04%), the effective interpretable rate is increased to 52.73% (↑11.45%). Additionally, it effectively reduces the difference between CV-based method and pathology in diagnosis results. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and our model outperforms pathologists with different levels of experience.

A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images.

The tumor grading of laryngeal cancer pathological images needs to be accurate and interpretable. The deep learning model based on the attention mechanism-integrated convolution (AMC) block has good inductive bias capability but poor interpretability, whereas the deep learning model based on the vision transformer (ViT) block has good interpretability but weak inductive bias ability. Therefore, we propose an end-to-end ViT-AMC network (ViT-AMCNet) with adaptive model fusion and multiobjective optimization that integrates and fuses the ViT and AMC blocks. However, existing model fusion methods often have negative fusion: 1). There is no guarantee that the ViT and AMC blocks will simultaneously have good feature representation capability. 2). The difference in feature representations learning between the ViT and AMC blocks is not obvious, so there is much redundant information in the two feature representations. Accordingly, we first prove the feasibility of fusing the ViT and AMC blocks based on Hoeffding's inequality. Then, we propose a multiobjective optimization method to solve the problem that ViT and AMC blocks cannot simultaneously have good feature representation. Finally, an adaptive model fusion method integrating the metrics block and the fusion block is proposed to increase the differences between feature representations and improve the deredundancy capability. Our methods improve the fusion ability of ViT-AMCNet, and experimental results demonstrate that ViT-AMCNet significantly outperforms state-of-the-art methods. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and the generalization ability is also excellent. Our code is publicly available at https://github.com/Baron-Huang/ViT-AMCNet.

Predictive models for starting antiseizure medication withdrawal following epilepsy surgery in adults.

More than half of adults with epilepsy undergoing resective epilepsy surgery achieve long-term seizure freedom and might consider withdrawing antiseizure medications. We aimed to identify predictors of seizure recurrence after starting postoperative antiseizure medication withdrawal and develop and validate predictive models. We performed an international multicentre observational cohort study in nine tertiary epilepsy referral centres. We included 850 adults who started antiseizure medication withdrawal following resective epilepsy surgery and were free of seizures other than focal non-motor aware seizures before starting antiseizure medication withdrawal. We developed a model predicting recurrent seizures, other than focal non-motor aware seizures, using Cox proportional hazards regression in a derivation cohort (n = 231). Independent predictors of seizure recurrence, other than focal non-motor aware seizures, following the start of antiseizure medication withdrawal were focal non-motor aware seizures after surgery and before withdrawal [adjusted hazard ratio (aHR) 5.5, 95% confidence interval (CI) 2.7-11.1], history of focal to bilateral tonic-clonic seizures before surgery (aHR 1.6, 95% CI 0.9-2.8), time from surgery to the start of antiseizure medication withdrawal (aHR 0.9, 95% CI 0.8-0.9) and number of antiseizure medications at time of surgery (aHR 1.2, 95% CI 0.9-1.6). Model discrimination showed a concordance statistic of 0.67 (95% CI 0.63-0.71) in the external validation cohorts (n = 500). A secondary model predicting recurrence of any seizures (including focal non-motor aware seizures) was developed and validated in a subgroup that did not have focal non-motor aware seizures before withdrawal (n = 639), showing a concordance statistic of 0.68 (95% CI 0.64-0.72). Calibration plots indicated high agreement of predicted and observed outcomes for both models. We show that simple algorithms, available as graphical nomograms and online tools (predictepilepsy.github.io), can provide probabilities of seizure outcomes after starting postoperative antiseizure medication withdrawal. These multicentre-validated models may assist clinicians when discussing antiseizure medication withdrawal after surgery with their patients.

Research on the relationship between the scattering contribution and physical factors of the reference radiation regulated by ISO 4037-1.

In the latest version of ISO 4037-1:2019 standard, the minimum dimension of a gamma radiation reference field was not clearly specified, which makes the construction of a minitype gamma reference radiation field lack of scientific basis. This paper carried out the research on the relationship between the scattering contribution and physical factors of the reference radiation regulated by ISO 4037-1. LS-SVM was applied to construct the relational model between physical factors and scattering contribution based on the data simulated by Monte Carlo method. Then the minimum dimension of collimated reference radiation field is obtained by PSO algorithm. For Co-60 source, the minimum size of the radiation field obtained is 93 cm(L)×40 cm(W)×40 cm(H). For Cs-137 source, the minimum size of the radiation field obtained is 153 cm(L)×47 cm (W)×47 cm(H). The results meet the requirements of the standard based on the model and provides a technical reference for the design of a minitype reference radiation field.

[Mechanism of Shaofu Zhuyu Decoction in treatment of endometriosis-associated dysmenorrhea with syndrome of cold coagulation and blood stasis based on MSK1/2].

This study aims to decipher the mechanism underlying the effect of Shaofu Zhuyu Decoction on endometriosis(EMT)-associated dysmenorrhea in rats with the syndrome of cold coagulation and blood stasis based on mitogen-and stress-activated protein kinase 1/2(MSK1/2).We employed a random number table to randomly assign SPF female non-pregnant rats into the sham group, and treated the rest rats with autologous transplantation+refrigerator freezing for the modeling of the syndrome of cold coagulation and blood stasis.The modeled rats were then randomly assigned into the control group and high-, medium-and low-dose Shaofu Zhuyu Decoction groups.The rats in the low-, medium-, and high-dose decoction groups were respectively administrated with 9, 4.5, and 2.3 g·kg~(-1) decoction through gavage once a day for 2 consecutive weeks, and those in the control group were administrated with 0.24 mg·kg~(-1) gestrinone through gavage once every 3 days for 2 weeks.After that, the size of ectopic focus in each rat was measured via laparotomy.Enzyme-linked immunosorbent assay(ELISA) was adopted to determine the expression of interleukin(IL)-6, IL-10, prostaglandin E2(PGE2), tumor necrosis factor-α(TNF-α).Western blot was employed to determine the protein levels of MSK1/2 and dual-specificity phosphatase 1(DUSP1) and real-time quantitative polymerase chain reaction(RT-PCR) to determine the mRNA levels of the two genes in rat eutopic endometrial tissue.Compared with the sham group, the model group showed increased levels of IL-6, PGE2, and TNF-α while decrease level of IL-10 in the serum(P<0.01).Compared with the model group, the high-and medium-dose decoction groups and the gestrinone group had declined levels of IL-6, PGE2, and TNF-α while risen level of IL-10 in the serum(P<0.01).The model group had lower protein levels and mRNA levels of MSK1/2 and DUSP1 in the eutopic endometrial tissue than the sham group(P<0.01). The high-and medium-dose decoction groups and the gestrinone group had higher protein and mRNA levels of MSK1/2 and DUSP1 in the eutopic endometrial tissue than the model group(P<0.01).The results indicated that Shaofu Zhuyu Decoction can regulate the abnormal expression of pro-inflammatory cytokines TNF-α, IL-6, and PGE2 and anti-inflammatory cytokines IL-10 and DUSP1 via MSK1/2 to alleviate EMT-associated dysmenorrhea in rats with the syndrome of cold coagulation and blood stasis.

GASP1 enhances malignant phenotypes of breast cancer cells and decreases their response to paclitaxel by forming a vicious cycle with IGF1/IGF1R signaling pathway.

There is a potential correlation between G-protein-coupled receptor-associated sorting protein 1 (GASP1) and breast tumorigenesis. However, its biological function and underlying molecular mechanism in breast cancer have not been clearly delineated. Here, we demonstrated that GASP1 was highly expressed in breast cancers, and patients harboring altered GASP1 showed a worse prognosis than those with wild-type GASP1. Functional studies showed that GASP1 knockout significantly suppressed malignant properties of breast cancer cells, such as inhibition of cell proliferation, colony formation, migration, invasion and xenograft tumor growth in nude mice as well as induction of G1-phase cell cycle arrest, and vice versa. Mechanistically, GASP1 inhibited proteasomal degradation of insulin-like growth factor 1 receptor (IGF1R) by competitively binding to IGF1R with ubiquitin E3 ligase MDM2, thereby activating its downstream signaling pathways such as NF-κB, PI3K/AKT, and MAPK/ERK pathways given their critical roles in breast tumorigenesis and progression. IGF1, in turn, stimulated GASP1 expression by activating the PI3K/AKT pathway, forming a vicious cycle propelling the malignant progression of breast cancer. Besides, we found that GASP1 knockout obviously improved the response of breast cancer cells to paclitaxel. Collectively, this study demonstrates that GASP1 enhances malignant behaviors of breast cancer cells and decreases their cellular response to paclitaxel by interacting with and stabilizing IGF1R, and suggests that it may serve as a valuable prognostic factor and potential therapeutic target in breast cancer.

Characterization of Different Subtypes of Immune Cell Infiltration in Glioblastoma to Aid Immunotherapy.

Glioblastoma multiforme (GBM) has been identified as a frequently occurring adult primary brain cancer that is highly aggressive. Currently, the prognostic outcome for GBM patients is dismal, even with intensive treatment, and the median overall survival (OS) is 14.6 months. Immunotherapy, which is specific at the cellular level and can generate persistent immunosurveillance, is now becoming a promising tool to treat diverse cancers. However, the complicated nature of the tumor microenvironment (TME) makes it challenging to develop anti-GBM immunotherapy because several cell types, cytokines, and signaling pathways are involved in generating the immunosuppressive environment. Novel immunotherapies can illustrate novel tumor-induced immunosuppressive mechanisms. Here, we used unsupervised clustering analysis to identify different subtypes of immune cell infiltration that actuated different prognoses, biological actions, and immunotherapy responses. Gene cluster A, with a hot immune cell infiltration phenotype, had high levels of immune-related genes (IRGs), which were associated with immune pathways including the interferon-gamma response and interferon-alpha response, and had low IDH1 and ATRX mutation frequencies. Gene cluster B, a cold immune cell infiltration subtype, exhibited a high expression of the KCNIP2, SCRT1, CPLX2, JPH3, UNC13A, GABRB3, ARPP21, DLGAP1, NRXN1, DLL3, CA10, MAP2, SEZ6L, GRIA2, and GRIA4 genes and a low expression of immune-related genes, i.e., low levels of immune reactivity. Our study highlighted the complex interplay between immune cell infiltration and genetic mutation in the establishment of the tumor immune phenotype. Gene cluster A was identified as an important subtype with a better prognosis and improved immunotherapy response.

Therapeutic application of GLP-1 and GIP receptor agonists in Parkinson's disease.

INTRODUCTION: Diabetes is a risk factor for Parkinson's disease (PD) and shares similar dysregulated insulin pathways. Glucagon-like peptide-1 (GLP-1) analogs originally designed to treat diabetes have shown potent neuroprotective activity in preclinical studies of PD. They are neuroprotective by inhibiting inflammation, improving neuronal survival, maintenance of synapses, and dopaminergic transmission in the brain. Building on this, three clinical studies have reported impressive effects in patients with PD, testing -4 (Exenatide, Bydureon) or liraglutide (Victoza, Saxenda). Glucose-dependent insulinotropic peptide (GIP) is another peptide hormone that has shown good effects in animal models of PD. Novel dual GLP-1/GIP agonists have been developed that can penetrate the blood-brain barrier (BBB) and show superior effects in animal models compared to GLP-1 drugs. AREAS COVERED: The review summarizes preclinical and clinical studies testing GLP-1R agonists and dual GLP-1/GIPR agonists in PD and discusses possible mechanisms of action. EXPERT OPINION: Current strategies to treat PD by lowering the levels of alpha-synuclein have not shown effects in clinical trials. It is time to move on from the 'misfolding protein' hypothesis. Growth factors such as GLP-1 that can cross the BBB have already shown impressive effects in patients and are the future of drug discovery in PD.

Retraction Notice to: Silencing Long Non-coding RNA LINC01224 Inhibits Hepatocellular Carcinoma Progression via MicroRNA-330-5p-Induced Inhibition of CHEK1.

[This retracts the article DOI: 10.1016/j.omtn.2019.10.007.].

Identification and validation of TNFRSF4 as a high-profile biomarker for prognosis and immunomodulation in endometrial carcinoma.

BACKGROUND: The interaction between tumor microenvironment (TME) and tumors offers various targets in mounting anti-tumor immunotherapies. However, the prognostic biomarkers in endometrial carcinoma (EC) are still limited. Here, we aimed to analyze the TME features and identify novel prognostic biomarkers for EC. METHODS: ESTIMATE, CIBERSORT, protein-protein interaction (PPI) network, univariate and multivariate Cox regression, and functional enrichment analysis were performed to identify immune- and survival-related hub genes as well as possible molecular mechanisms. The limma package and deconvolution algorithm were adopted to estimate the abundance of tumor-infiltrating immune cells (TICs) and their relationship with the target gene. In the validation section, tissue microarrays (TMAs) of EC and multiplex immunohistochemistry (m-IHC) were evaluated to validate the expression of TNFRSF4, and its correlation with immune markers, including CD4, CD8, and FOXP3. Besides, the receiver operating characteristic (ROC) curve was plotted to determine the diagnostic performance of TNFRSF4, CD4, CD8, and FOXP3 in EC. RESULTS: Two genes, TNFRSF4 and S1PR4, were screened out from 386 intersection differential expression genes (DEGs) shared by ImmuneScore and StromalScore in EC. Highlighted by TNFRSF4, we found that it was not only positively correlated with the TICs (mainly CD4+ T cells, CD8+ T cells, and Tregs) but significantly related to the prognosis in patients of EC, both verified by data from The Cancer Genome Altas (TCGA)-EC database and clinical samples. At the same time, the expression trend of TNFRSF4 was further confirmed by an integrated meta-analysis based on six microarrays from the Gene Expression Omnibus database (GEO). CONCLUSIONS: Collectively, TNFRSF4, a previously unrecognized key player in EC, could serve as a potential biomarker for prognosis prediction and immunomodulation of EC.

Integrative Analysis of Multi-Omics Data-Identified Key Genes With KLRC3 as the Core in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma.

In a recent study, the PD-1 inhibitor has been widely used in clinical trials and shown to improve various cancers. However, PD-1/PD-L1 inhibitors showed a low response rate and were effective for only a small number of cancer patients. Thus, it is important to figure out the issue about the low response rate of immunotherapy. Here, we performed ssGSEA and unsupervised clustering analysis to identify three clusters (clusters A, B, and C) according to different immune cell infiltration status, prognosis, and biological action. Of them, cluster C showed a better survival rate, higher immune cell infiltration, and immunotherapy effect, with enrichment of a variety of immune active pathways including T and B cell signal receptors. In addition, it showed more significant features associated with immune subtypes C2 and C3. Furthermore, we used WGCNA analysis to confirm the cluster C-associated genes. The immune-activated module highly correlated with 111 genes in cluster C. To pick candidate genes in SD/PD and CR/PR patients, we used the least absolute shrinkage (LASSO) and SVM-RFE algorithms to identify the targets with better prognosis, activated immune-related pathways, and better immunotherapy. Finally, our analysis suggested that there were six genes with KLRC3 as the core which can efficiently improve immunotherapy responses with greater efficacy and better prognosis, and our study provided clues for further investigation about target genes associated with the higher response rate of immunotherapy.