Titration of RAS alters senescent state and influences tumour initiation.

Oncogenic RAS-induced senescence (OIS) is an autonomous tumour suppressor mechanism associated with premalignancy1,2. Achieving this phenotype typically requires a high level of oncogenic stress, yet the phenotype provoked by lower oncogenic dosage remains unclear. Here we develop oncogenic RAS dose-escalation models in vitro and in vivo, revealing a RAS dose-driven non-linear continuum of downstream phenotypes. In a hepatocyte OIS model in vivo, ectopic expression of NRAS(G12V) does not induce tumours, in part owing to OIS-driven immune clearance3. Single-cell RNA sequencing analyses reveal distinct hepatocyte clusters with typical OIS or progenitor-like features, corresponding to high and intermediate levels of NRAS(G12V), respectively. When titred down, NRAS(G12V)-expressing hepatocytes become immune resistant and develop tumours. Time-series monitoring at single-cell resolution identifies two distinct tumour types: early-onset aggressive undifferentiated and late-onset differentiated hepatocellular carcinoma. The molecular signature of each mouse tumour type is associated with different progenitor features and enriched in distinct human hepatocellular carcinoma subclasses. Our results define the oncogenic dosage-driven OIS spectrum, reconciling the senescence and tumour initiation phenotypes in early tumorigenesis.

Senescence-induced endothelial phenotypes underpin immune-mediated senescence surveillance.

Senescence is a stress-responsive tumor suppressor mechanism associated with expression of the senescence-associated secretory phenotype (SASP). Through the SASP, senescent cells trigger their own immune-mediated elimination, which if evaded leads to tumorigenesis. Senescent parenchymal cells are separated from circulating immunocytes by the endothelium, which is targeted by microenvironmental signaling. Here we show that SASP induces endothelial cell NF-κB activity and that SASP-induced endothelial expression of the canonical NF-κB component Rela underpins senescence surveillance. Using human liver sinusoidal endothelial cells (LSECs), we show that SASP-induced endothelial NF-κB activity regulates a conserved transcriptional program supporting immunocyte recruitment. Furthermore, oncogenic hepatocyte senescence drives murine LSEC NF-κB activity in vivo. Critically, we show two distinct endothelial pathways in senescence surveillance. First, endothelial-specific loss of Rela prevents development of Stat1-expressing CD4+ T lymphocytes. Second, the SASP up-regulates ICOSLG on LSECs, with the ICOS-ICOSLG axis contributing to senescence cell clearance. Our results show that the endothelium is a nonautonomous SASP target and an organizing center for immune-mediated senescence surveillance.

Allele-specific RNA N 6-methyladenosine modifications reveal functional genetic variants in human tissues.

An intricate network of cis- and trans-elements acts on RNA N 6-methyladenosine (m6A), which in turn may affect gene expression and, ultimately, human health. A complete understanding of this network requires new approaches to accurately measure the subtle m6A differences arising from genetic variants, many of which have been associated with common diseases. To address this gap, we developed a method to accurately and sensitively detect transcriptome-wide allele-specific m6A (ASm6A) from MeRIP-seq data and applied it to uncover 12,056 high-confidence ASm6A modifications from 25 human tissues. We also identified 1184 putative functional variants for ASm6A regulation, a subset of which we experimentally validated. Importantly, we found that many of these ASm6A-associated genetic variants were enriched for common disease-associated and complex trait-associated risk loci, and verified that two disease risk variants can change m6A modification status. Together, this work provides a tool to detangle the dynamic network of RNA modifications at the allelic level and highlights the interplay of m6A and genetics in human health and disease.

miTDS: Uncovering miRNA-mRNA interactions with deep learning for functional target prediction.

MicroRNAs (miRNAs) are vital in regulating gene expression through binding to specific target sites on messenger RNAs (mRNAs), a process closely tied to cancer pathogenesis. Identifying miRNA functional targets is essential but challenging, due to incomplete genome annotation and an emphasis on known miRNA-mRNA interactions, restricting predictions of unknown ones. To address those challenges, we have developed a deep learning model based on miRNA functional target identification, named miTDS, to investigate miRNA-mRNA interactions. miTDS first employs a scoring mechanism to eliminate unstable sequence pairs and then utilizes a dynamic word embedding model based on the transformer architecture, enabling a comprehensive analysis of miRNA-mRNA interaction sites by harnessing the global contextual associations of each nucleotide. On this basis, miTDS fuses extended seed alignment representations learned in the multi-scale attention mechanism module with dynamic semantic representations extracted in the RNA-based dual-path module, which can further elucidate and predict miRNA and mRNA functions and interactions. To validate the effectiveness of miTDS, we conducted a thorough comparison with state-of-the-art miRNA-mRNA functional target prediction methods. The evaluation, performed on a dataset cross-referenced with entries from MirTarbase and Diana-TarBase, revealed that miTDS surpasses current methods in accurately predicting functional targets. In addition, our model exhibited proficiency in identifying A-to-I RNA editing sites, which represents an aberrant interaction that yields valuable insights into the suppression of cancerous processes.

Improving stroke prognosis by TLR4 KO to enhance N2 neutrophil infiltration and reduce M1 macrophage polarization.

Cerebral ischemic stroke remains a leading cause of mortality and morbidity worldwide. Toll-like receptor 4 (TLR4) has been implicated in neuroinflammatory responses poststroke, particularly in the infiltration of immune cells and polarization of macrophages. This study aimed to elucidate the impact of TLR4 deficiency on neutrophil infiltration and subsequent macrophage polarization after middle cerebral artery occlusion (MCAO), exploring its role in stroke prognosis. The objective was to investigate how TLR4 deficiency influences neutrophil behavior poststroke, its role in macrophage polarization, and its impact on stroke prognosis using murine models. Wild-type and TLR4-deficient adult male mice underwent MCAO induction, followed by various analyses, including flow cytometry to assess immune cell populations, bone marrow transplantation experiments to evaluate TLR4-deficient neutrophil behaviors, and enzyme-linked immunosorbent assay and Western blot analysis for cytokine and protein expression profiling. Neurobehavioral tests and infarct volume analysis were performed to assess the functional and anatomical prognosis poststroke. TLR4-deficient mice exhibited reduced infarct volumes, increased neutrophil infiltration, and reduced M1-type macrophage polarization post-MCAO compared to wild-type mice. Moreover, the depletion of neutrophils reversed the neuroprotective effects observed in TLR4-deficient mice, suggesting the involvement of neutrophils in mediating TLR4's protective role. Additionally, N1-type neutrophils were found to promote M1 macrophage polarization via neutrophil gelatinase-associated lipocalin (NGAL) secretion, a process blocked by TLR4 deficiency. The study underscores the protective role of TLR4 deficiency in ischemic stroke, delineating its association with increased N2-type neutrophil infiltration, diminished M1 macrophage polarization, and reduced neuroinflammatory responses. Understanding the interplay between TLR4, neutrophils, and macrophages sheds light on potential therapeutic targets for stroke management, highlighting TLR4 as a promising avenue for intervention in stroke-associated neuroinflammation and tissue damage.

Risk assessment for femoral head collapse in osteonecrosis utilizing MRI-derived large lesion ratio: a retrospective cohort study.

PURPOSE: This study aimed to developed a novel and practical method to quantify the involvement of lesion in osteonecrosis of the femoral head (ONFH). We hypothesized that the new metric large lesion ratio (LLR) had promising prognostic value. METHODS: A total of 131 hips with non-traumatic ONFH were included in this retrospective study. Patient aged 18-60 with MRI-confirmed diagnosis, and a minimum of 2-year follow-up or radiographic collapse progression during follow-up were included. Patients with prior hip surgery, incomplete data or advanced ONFH at baseline (femoral head collapse > 2 mm or osteoarthritis) were excluded. Involvement of necrotic lesion was evaluated by calculating LLR. The differences of LLR between collapse progression and non-progression groups were investigated, and the differences among different scanning parameters groups were also examined. Prognostic value of LLR was examined by multivariate regression analysis. Receiver operating characteristic curves (ROC) were constructed and areas under the curve (AUC) were compared. RESULTS: The median of LLR was 66.67% in the collapse progression group, which was significantly higher compared with 25.00% in the non-progression group (P < 0.001). Subgroups analysis showed that LLR were significantly higher in the collapse progression group of Japanese Investigation Committee type C1 (P < 0.001)and C2 (P = 0.002). Multivariate regression showed that LLR were independently correlated with collapse progression (OR, 1.46 [95% CI, 1.24-1.78]; P < 0.001). ROC analysis showed that the AUC for LLR was 0.84, outperforming the 0.74 AUC OF the JIC classification. CONCLUSION: LLR could served as a efficient tool to assess the risk of collapse progression and guide the selection of treatment strategy.

Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI.

BACKGROUND: Femoral head collapse is a critical pathological change and is regarded as turning point in disease progression in osteonecrosis of the femoral head (ONFH). In this study, we aim to build an automatic femoral head collapse prediction pipeline for ONFH based on magnetic resonance imaging (MRI) radiomics. METHODS: In the segmentation model development dataset, T1-weighted MRI of 222 hips from two hospitals were retrospectively collected and randomly split into training (n = 190) and test (n = 32) sets. In the prognosis prediction model development dataset, 206 hips were also retrospectively collected from two hospitals and divided into training set (n = 155) and external test set (n = 51) according to data source. A deep learning model for automatic lesion segmentation was trained with nnU-Net, from which three-dimensional regions of interest were segmented and a total of 107 radiomics features were extracted. After intra-class correlation coefficients screening, feature correlation coefficient screening and Least Absolute Shrinkage and Selection Operator regression feature selection, a machine learning model for ONFH prognosis prediction was trained with Logistic Regression (LR) and Light Gradient Boosting Machine (LightGBM) algorithm. RESULTS: The segmentation model achieved an average dice similarity coefficient of 0.848 and an average 95% Hausdorff distance of 3.794 in the test set, compared to the manual segmentation results. After feature selection, nine radiomics features were included in the prognosis prediction model. External test showed that the LightGBM model exhibited acceptable predictive performance. The area under the curve (AUC) of the prediction model was 0.851 (95% CI: 0.7268-0.9752), with an accuracy of 0.765, sensitivity of 0.833, and specificity of 0.727. Decision curve analysis showed that the LightGBM model exhibited favorable clinical utility. CONCLUSION: This study presents an automated pipeline for predicting femoral head collapse in ONFH with acceptable performance. Further research is necessary to determine the clinical applicability of this radiomics-based approach and to assess its potential to assist in treatment decision-making for ONFH.

[A study on the regulation of motor behavior in mouse based on temporal interference].

Temporal interference (TI) as a new neuromodulation technique can be applied to non-invasive deep brain stimulation. In order to verify its effectiveness in the regulation of motor behavior in animals, this paper uses the TI method to focus the envelope electric field to the ventral posterior lateral nucleus (VPL) of the thalamus in the deep brain of mouse to regulate left- and right-turning motor behavior. The focusability of TI in the mouse VPL was analyzed by finite element method, and the focus area and volume were obtained by numerical calculation. A stimulator was used to generate TI current to stimulate the mouse VPL to verify the effectiveness of the TI stimulation method, and the accuracy of the focus location was further determined by c-Fos immunofluorescence experiments. The results showed that the electric field generated by TI stimulation was able to focus on the VPL nuclei when the stimulation current reached 800 µA; the mouse were able to make corresponding left and right turns according to the stimulation position; and the c-Fos positive cell markers in the VPL nuclei increased significantly after stimulation. This study confirms the feasibility of TI in regulating animal motor behavior and provides a non-invasive stimulation method for brain tissue for animal robots.

Harnessing the Reactivity of Nitroarene Radical Anions to Create Quinoline N-Oxides by Electrochemical Reductive Cyclization.

Electrochemical reduction of 2-allyl-substituted nitroarenes using a simple, undivided electrochemical cell with non-precious electrodes to generate nitroarene radical anions was developed. The nitroarene radical anion intermediates participate in 1,5-hydrogen atom transfer reactions to construct quinoline N-oxides bearing aryl-, heteroaryl-, alkenyl-, benzyl-, sulfonyl-, or carboxyl groups.

A novel m7G regulator-based methylation patterns in head and neck squamous cell carcinoma.

Abnormal RNA N7-methylguanosine (m7G) modification is known to contribute to effects on tumor occurrence and development. Nevertheless, the mechanisms of its function in immunoregulation, tumor microenvironment (TME) modulation, and tumor promotion remain largely unknown. A series of computer-aided bioinformatic analyses were conducted based on transcriptomic, single-cell sequence, and spatial transcriptomic data to determine the m7G modification patterns in head and neck squamous cell carcinoma (HNSCC). Consensus clustering approach was employed according to the expressions of 33 m7G regulators. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis algorithms were adopted to investigate the immune cell infiltration features. A prognostic model named m7Gscore was established. Seurat, SingleR, and Monocle2 were used to analyze the single-cell sequence profiling. STUtility was used to integrate multiple spatial transcriptomic datasets. Quantitative reverse transcription polymerase chain reaction, transwell, and wound-healing assay were performed to verify the oncogenes. Here, three different m7G modification patterns were highlighted in HNSCC patients, which were also related to various clinical manifestations and three representative immunophenotypes: immune-excluded, immune-desert, and inflamed, separately. Patients with lower m7Gscore were highlighted by higher immune cell infiltrations, better overall survival rates, lesser tumor mutation burden (TMB), lower sensitivities to target inhibitors therapies, and better immunotherapeutic response. Moreover, DCPS, EIF4E, EIF4E2, LSM1, NCBP2, NUDT1, and NUDT5 were identified to play critical roles in T-cell differentiation. Knockdown of LSM1/NUDT5 could restrain the malignancy of HNSCC cells. Collectively, quantitative assessment of m7G modification patterns in individual HNSCC patients could contribute to identifying more efficient immunotherapeutic approaches and improve the clinical outcome of HNSCC.

DDIT4 promotes malignancy of head and neck squamous cell carcinoma.

This study investigated the cancer-promoting effect of ferroptosis regulator DNA damage-inducible transcript 4 (DDIT4) and its relevant mechanisms. Vital ferroptosis-related genes were identified using bioinformatic methods on the basis of data collected from TCGA and seven other online databases. Cell Counting Kit-8 (CCK8), colony formation, wound-healing and transwell assays, and western blot analysis were conducted for verifying the biological role of DDIT4 in vitro. The immune score and tumor purity were calculated using R package "estimate." The relationship was identified between DDIT4 expression and immune cell infiltration using ssGSEA and CIBERSORT algorithms. R package "Seurat" was used to perform unsupervised clustering of the single cells, and "SingleR" was utilized for annotation. R package "STUtility" was employed to plot the spatial expression of DDIT4. For trajectory analysis, monocle was used to predict cell differentiation and demonstrate the expression of DDIT4 at each state. Here, DDIT4 overexpression was observed in Head and Neck Squamous Cell Carcinoma (HNSCC) cohort, and DDIT4 upregulation showed a positive correlation with larger tumor size, lymph node metastasis, more advanced TNM stage and higher tumor mutational burden (TMB). Moreover, DDIT4 knockdown could markedly inhibit the proliferation, colony formation, invasion and migration of HNSCC cells, as well as suppress the expression of HIF-1a, VEGF and vimentin. In comparison, DDIT4 overexpression showed a negative correlation with immune score and infiltrations of several immune cells. DDIT4 played crucial roles in the differentiation of CAFs and T cells. Collectively, this study demonstrates that DDIT4 contributes a critical role in HNSCC progression. The positive feedback regulation between DDIT4 and HIF-1a may be a potential target for HNSCC treatment.

m7G-related genes-NCBP2 and EIF4E3 determine immune contexture in head and neck squamous cell carcinoma by regulating CCL4/CCL5 expression.

Aberrant N7 -methylguanosine (m7G) levels closely correlate with tumor genesis and progression. NCBP2 and EIF4E3 are two important m7G-related cap-binding genes. This study aimed to identify the relationship between the EIF4E3/NCBP2 function and immunological characteristics of head and neck squamous cell carcinoma (HNSCC). Hierarchical clustering was employed in classifying HNSCC patients into two groups based on the expressions of NCBP2 and EIF4E3. The differentially expressed genes were identified between the two groups, and GO functional enrichment was subsequently performed. Weighted gene co-expression network analysis was conducted to identify the hub genes related to EIF4E3/NCBP2 expression and immunity. The differential infiltration of immune cells and the response to immunotherapy were compared between the two groups. Single-cell sequence and trajectory analyses were performed to predict cell differentiation and display the expression of EIF4E3/NCBP2 in each state. In addition, quantitative real-time PCR, spatial transcriptome analysis, transwell assay, and western blotting were conducted to verify the biological function of EIF4E3/NCBP2. Here, group A showed a higher EIF4E3 expression and a lower NCBP2 expression, which had higher immune scores, proportion of most immune cells, immune activities, expression of immunomodulatory targets, and a better response to cancer immunotherapy. Besides, 56 hub molecules with notable immune regulation significance were identified. A risk model containing 17 hub genes and a prognostic nomogram was successfully established. Moreover, HNSCC tissues had a lower EIF4E3 expression and a higher NCBP2 expression than normal tissues. NCBP2 and EIF4E3 played a vital role in the differentiation of monocytes. Furthermore, the expression of CCL4/CCL5 can be regulated via EIF4E3 overexpression and NCBP2 knockdown. Collectively, NCBP2 and EIF4E3 can affect downstream gene expression, as well as immune contexture and response to immunotherapy, which could induce "cold-to-hot" tumor transformation in HNSCC patients.

Molecular mechanisms of miR172a and its target gene LbrTOE3 regulating maturation in Lilium.

MAIN CONCLUSION: Lbr-miR172a could promote the growth phase transition and shorten maturation in Lilium, while LbrTOE3 inhibited this process and prolonged the growth period. Lilium is an ornamental flower with high economic value for both food and medicinal purposes. However, under natural conditions, Lilium bulbs take a long time and cost more to grow to commercial size. This research was conducted to shorten the maturation time by subjecting Lilium bulbs to alternating temperature treatment. To explore the molecular mechanism of the vegetative phase change (VPC) in Lilium after variable temperature treatment, the key module miR172a-TOE3 was selected based on a combined omics analysis. Gene cloning and transgene functional validation showed that overexpression of Lbr-mir172a promoted a phase change, while overexpression of LbrTOE3 inhibited this process. Subcellular localization and transcriptional activation assays indicated that LbrTOE3 was predominantly localized in the nucleus and showed transcriptional activity. In situ hybridization showed that LbrTOE3 expression was significantly downregulated after alternating temperature treatment. This study elucidates the molecular mechanisms of the phase transition of Lilium and provides a scientific basis for the phase transition in other plants.

Simulation Investigation on the Influence Mechanism of Toluene and Heptane on the Aggregation of Asphaltene Molecules.

Asphaltenes are a group of compounds that are soluble in benzene and toluene but insoluble in nonpolar small molecule n-alkanes. The asphaltene aggregation in the asphaltene-heptane-toluene system was studied using molecular dynamics (MD) simulation, and the interaction between asphaltene molecules during this process was also revealed from the evolution of the density field, radial distribution function (RDF), and intermolecular distance of asphaltenes. Three main findings were made: (1) more asphaltene precipitates (heptane) were contained, and more asphaltene dimers or trimers were formed during the MD simulation; (2) asphaltene molecules interacted with each other to form aggregates in the form of π-π or H-bond interaction. The stable distance of the π-π interaction was 3.3-3.5 Å, and the stable distance of the H-bond connection was 1.7-1.9 Å. (3) The asphaltene interaction in the heptane-rich system was dominated by π-π interaction between asphaltene molecules. However, the asphaltene interactions in the toluene-rich system were mainly the π-π interaction between asphaltene molecules and toluene and the H-bond interaction between the side chains of asphaltene molecules. The results of this study can aid in understanding how asphaltene molecules aggregate and self-associate and can also offer theoretical support for flow assurance in systems used to produce crude oil.

Pressure-Sensitive Capability of AgNPs Self-Sensing Cementitious Sensors.

Intelligent monitoring approaches for long-term, real-time digitalization in structural health monitoring (SHM) are currently attracting significant interest. Among these, self-sensing cementitious composites stand out due to their easy preparation, cost-effectiveness, and excellent compatibility with concrete structures. However, the current research faces challenges, such as excessive conductive filler, difficulties in filler dispersion, and insufficient stress sensitivity and instability. This study presents a novel approach to these challenges by fabricating self-sensing cementitious sensors using silver nanoparticles (AgNPs), a new type of conductive filler. The percolation threshold of AgNPs in these materials was determined to be 0.0066 wt%, marking a reduction of approximately 90% compared to traditional conductive fillers. Moreover, the absorbance test with a UV spectrophotometer showed that AgNPs were well dispersed in an aqueous solution, which is beneficial for the construction of conductive pathways. Through various cyclic loading tests, it was observed that the self-sensing cementitious sensors with AgNPs exhibited robust pressure-sensitive stability. Additionally, their stress sensitivity reached 11.736, a value significantly surpassing that of conventional fillers. Regarding the conductive mechanism, when encountering the intricate environment within the cementitious material, AgNPs can establish numerous conductive pathways, ensuring a stable response to stress due to their ample quantity. This study provides a significant contribution to addressing the existing challenges in self-sensing cementitious materials and offers a novel reference for further research in this domain.

Structural Insights into the Binding of Red Fluorescent Protein mCherry-Specific Nanobodies.

Red fluorescent proteins (RFPs) have broad applications in life science research, and the manipulation of RFPs using nanobodies can expand their potential uses. However, the structural information available for nanobodies that bind with RFPs is still insufficient. In this study, we cloned, expressed, purified, and crystallized complexes formed by mCherry with LaM1, LaM3, and LaM8. Then, we analyzed the biochemical properties of the complexes using mass spectrometry (MS), fluorescence-detected size exclusion chromatography (FSEC), isothermal titration calorimetry (ITC), and bio-layer interferometry (BLI) technology. We determined the crystal structure of mCherry-LaM1, mCherry-LaM3, and mCherry-LaM8, with resolutions of 2.05 Å, 3.29 Å, and 1.31 Å, respectively. In this study, we systematically compared various parameters of several LaM series nanobodies, including LaM1, LaM3, and LaM8, with previously reported data on LaM2, LaM4, and LaM6, specifically examining their structural information. After designing multivalent tandem LaM1-LaM8 and LaM8-LaM4 nanobodies based on structural information, we characterized their properties, revealing their higher affinity and specificity to mCherry. Our research provides novel structural insights that could aid in understanding nanobodies targeting a specific target protein. This could provide a starting point for developing enhanced mCherry manipulation tools.

Position-Wise Gated Res2Net-Based Convolutional Network with Selective Fusing for Sentiment Analysis.

Sentiment analysis (SA) is an important task in natural language processing in which convolutional neural networks (CNNs) have been successfully applied. However, most existing CNNs can only extract predefined, fixed-scale sentiment features and cannot synthesize flexible, multi-scale sentiment features. Moreover, these models' convolutional and pooling layers gradually lose local detailed information. In this study, a new CNN model based on residual network technology and attention mechanisms is proposed. This model exploits more abundant multi-scale sentiment features and addresses the loss of locally detailed information to enhance the accuracy of sentiment classification. It is primarily composed of a position-wise gated Res2Net (PG-Res2Net) module and a selective fusing module. The PG-Res2Net module can adaptively learn multi-scale sentiment features over a large range using multi-way convolution, residual-like connections, and position-wise gates. The selective fusing module is developed to fully reuse and selectively fuse these features for prediction. The proposed model was evaluated using five baseline datasets. The experimental results demonstrate that the proposed model surpassed the other models in performance. In the best case, the model outperforms the other models by up to 1.2%. Ablation studies and visualizations further revealed the model's ability to extract and fuse multi-scale sentiment features.

[Characterization of Cell Subsets Associated With Prognosis of Osteosarcoma Based on Single-Cell Sequencing Data].

Objective To explore the cell subsets and characteristics related to the prognosis of osteosarcoma by analyzing the cellular composition of tumor tissue samples from different osteosarcoma patients.Methods The single-cell sequencing data and bulk sequencing data of different osteosarcoma patients were downloaded.We extracted the information of cell samples for dimensionality reduction,annotation,and cell function analysis,so as to identify the cell subsets and clarify the cell characteristics related to the prognosis of osteosarcoma.The development trajectory of macrophages with prognostic significance was analyzed,and the prognostic model of osteosarcoma was established based on the differentially expressed genes of macrophage differentiation.Results The cellular composition presented heterogeneity in the patients with osteosarcoma.The infiltration of mononuclear phagocytes in osteosarcoma had prognostic significance(P=0.003).Four macrophage subsets were associated with prognosis,and their signature transcription factors included RUNX3(+),ETS1(+),HOXD11(+),ZNF281(+),and PRRX1(+).Prog_Macro2 and Prog_Macro4 were located at the end of the developmental trajectory,and the prognostic ability of macrophage subsets increased with the progression of osteosarcoma.The prognostic model established based on the differentially expressed genes involved in macrophage differentiation can distinguish the survival rate of osteosarcoma patients with different risks(P<0.001).Conclusion Macrophage subsets are closely related to the prognosis of osteosarcoma and can be used as the key target cells for the immunotherapy of osteosarcoma.

Influence of optical zone on myopic correction in small incision lenticule extraction: a short-term study.

BACKGROUND: To evaluate the influence of preoperative optical zone on myopic correction in small incision lenticule extraction. METHODS: In this retrospective clinical study, 581 eyes from 316 patients underwent SMILE were selected, including 117 eyes in the small optical zone group (range from 6.0 to 6.4 mm) and 464 eyes in the large optical zone group (range from 6.5 to 6.8 mm). The measurements included uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), spherical, and cylinder were measured preoperatively and 3 months postoperatively. Propensity score match (PSM) analysis was performed with age, gender, eye (right/left), keratometry and preoperative spherical equivalent between two different groups. The influence of optical zones on postoperative refractive outcomes were evaluated using univariate regression analysis. RESULTS: In total, 78 pairs of eyes were selected by PSM (match ratio 1:1). There were no differences in the age, gender, eye (right/left), keratometry or preoperative spherical equivalent between the small and large optical zone groups. However, the difference of postoperative spherical equivalent was significantly between groups. Patients with larger optical zones had a trend towards less undercorrection (P = 0.018). Univariate linear regression model analysis found that each millimeter larger optical zone resulted in 8.13% or 0.39D less undercorrection (P < 0.001). The dependency between the optical zones and postoperative spherical equivalent was significant in the higher preoperative myopia group (r = 0.281, P < 0.001), but not significant in the lower myopia group (r = 0.028, P = 0.702). CONCLUSION: The diameter of optical zones would affect postoperative refractive outcomes in small incision lenticule extraction. This study indicated that larger optical zones induced less undercorrection, especially in patients with high myopia.

Anlotinib attenuates experimental autoimmune encephalomyelitis mice model of multiple sclerosis via modulating the differentiation of Th17 and Treg cells.

BACKGROUND: In multiple sclerosis (MS), the imbalance between T helper (Th)-17 cells and regulatory T (Treg) cells are critical in autoimmune central nervous system (CNS) inflammation and demyelination. Experimental autoimmune encephalomyelitis (EAE) is an established mouse MS model and simulates MS at diverse levels. OBJECTIVES: This study aims at investigating the impact of anlotinib on the clinical severity of EAE and CD4+ T cell differentiation. MATERIALS AND METHODS: EAE-induced mice were treated with water (control) or 6 mg/kg anlotinib by gavage daily. At the peak of EAE, histopathological examination and flow cytometry analysis of CNS-infiltrating CD4+ T cells were performed. In vitro differentiation of CD4+ T cells under different conditions was detected by flow cytometry and quantitative real-time PCR. Finally, the impacts of anlotinib on the phosphorylation of signal transducer and activator of transcription 3 (STAT3) and the transcription levels of key genes involved in Th17 and Treg differentiation were tested. RESULTS: Anlotinib attenuated the clinical severity of EAE and changed the frequencies of CNS-infiltrating CD4+ T cell subsets. Anlotinib inhibited the differentiation of Th17 cells in vitro, decreased the phosphorylation of STAT3, and reduced the expression of Rorc. Anlotinib promoted the differentiation of Treg cells and upregulated the expression levels of CD39 and CD73. DISCUSSION AND CONCLUSIONS: Anlotinib alleviated the symptoms of EAE via inhibiting the Th17 cell differentiation and promoting Treg cell differentiation. Our study provides new opportunities for the exploitation of anlotinib as a therapeutic agent for the treatment of MS.