An innovative prognostic model based on autophagy-related long noncoding RNA signature for low-grade glioma.

Autophagy is a highly conserved lysosomal degradation process essential in tumorigenesis. However, the involvement of autophagy-related long noncoding RNAs (lncRNAs) in low-grade glioma (LGG) remains unclear. In this study, we established an autophagy-related lncRNA prognostic signature for patients with LGG and assess its underlying functions. We used univariate Cox, least absolute shrinkage and selection operator and multivariate Cox regression models to establish an autophagy-related lncRNA prognostic signature. Kaplan-Meier survival analysis, receiver operating characteristic curve, nomogram, C-index, calibration curve and clinical decision-making curve were used to assess the predictive capability of the identified signature. A signature comprising nine autophagy-related lncRNAs (AL136964.1, ARHGEF26-AS1, PCED1B-AS1, AS104072.1, PRKCQ-AS1, LINC00957, AS125616.1, PSMB8-AS1 and AC087741.1) was identified as a prognostic model. Patients with LGG were divided into the high- and low-risk cohorts based on the median model-based risk score. The survival analysis revealed a 10-year survival rate of 9.3% (95% CI 1.91-45.3%) and 13.48% (95% CI 4.52-40.2%) in high-risk patients in the training and validation sets, respectively, and 48.4% (95% CI 24.7-95.0%) and 48.4% (95% CI 28.04-83.4%) in low-risk patients in the training and validation sets, respectively. This finding suggested a relatively low survival in high-risk patients. In addition, the lncRNA signature was independently prognostic and potentially associated with the progression of LGG. Therefore, the 9-autophagy-related-lncRNA signature may play a crucial role in the diagnosis and treatment of LGG, which may offer new avenues for tumour-targeted therapy.

Integrated Addressable Dynamic Droplet Array (aDDA) as Sub-Nanoliter Reactors for High-Coverage Genome Sequencing of Single Yeast Cells.

An addressable dynamic droplet array (aDDA) is presented that combines the advantages of static droplet arrays and continuous-flow droplet platforms. Modular fabrication is employed to create a self-contained integrated aDDA. All the sample preparation steps, including single-cell isolation, cell lysis, amplification, and product retrieval, are performed in sequence within a sub-nanoliter (≈300 pL) droplet. Sequencing-based validation suggests that aDDA reduces the amplification bias of multiple displacement amplification (MDA) and elevates the percentage of one-yeast-cell genome recovery to 91%, as compared to the average of 26% using conventional, 20 µL volume MDA reactions. Thus, aDDA is a valuable addition to the toolbox for high-genome-coverage sequencing of single microbial cells.

One-step liquid molding based modular microfluidic circuits.

Modular concepts open a new way to create customized integrated microfluidic devices for the changing needs of users. However, easy-to-follow modular construction at the micro-scale remains a crucial challenge. Here, we present a one-step liquid molding based modular method. Liquid molding was coupled with standard SU-8 lithography to fabricate the connection adapters and the intricate micro-flow networks of modules. The connection adapter in each of the modules with three-dimensional topographic structures bridges the gap between the macroscopic world and the microfluidic network. Analogous to electronic circuits, individual functional components were assembled together via the standard fused silica capillary tubing in series or parallel, forming a leak-free integrated whole. The modular microfluidic circuits were further applied to pathogenic bacteria detection and parallel droplet generation. Via these applications, we demonstrated that modular circuits can be easily assembled and disassembled, thus enabling easy reconfiguration. Additionally, the ability to incorporate components made from different materials was exhibited.

Smartphone-based rapid quantification of viable bacteria by single-cell microdroplet turbidity imaging.

Standard plate count (SPC) has been recognized as the golden standard for the quantification of viable bacteria. However, SPC usually takes one to several days to grow individual cells into a visible colony, which greatly hampers its application in rapid bacteria enumeration. Here we present a microdroplet turbidity imaging based digital standard plate count (dSPC) method to overcome this hurdle. Instead of cultivating on agar plates, bacteria are encapsulated in monodisperse microdroplets for single-cell cultivation. Proliferation of the encapsulated bacterial cell produced a detectable change in microdroplet turbidity, which allowed, after just a few bacterial doubling cycles (i.e., a few hours), enumeration of viable bacteria by visible-light imaging. Furthermore, a dSPC platform integrating a power-free droplet generator with smartphone-based turbidity imaging was established. As proof-of-concept demonstrations, a series of Gram-negative bacteria (Escherichia coli) and Gram-positive bacteria (Bacillus subtilis) samples were quantified via the smartphone dSPC accurately within 6 hours, representing a detection sensitivity of 100 CFU ml-1 and at least 3 times faster. In addition, Enterobacter sakazakii (E. sakazakii) in infant milk powder as a real sample was enumerated within 6 hours, in contrast to the 24 hours needed in traditional SPC. Results with high accuracy and reproducibility were achieved, with no difference in counts found between dSPC and SPC. By enabling label-free, rapid, portable and low-cost enumeration and cultivation of viable bacteria onsite, smartphone dSPC forms the basis for a temporally and geographically trackable network for surveying live microbes globally where every citizen with a cellphone can contribute anytime and anywhere.

Raman-Activated Droplet Sorting (RADS) for Label-Free High-Throughput Screening of Microalgal Single-Cells.

Raman-activated cell sorting (RACS) has attracted increasing interest, yet throughput remains one major factor limiting its broader application. Here we present an integrated Raman-activated droplet sorting (RADS) microfluidic system for functional screening of live cells in a label-free and high-throughput manner, by employing AXT-synthetic industrial microalga Haematococcus pluvialis (H. pluvialis) as a model. Raman microspectroscopy analysis of individual cells is carried out prior to their microdroplet encapsulation, which is then directly coupled to DEP-based droplet sorting. To validate the system, H. pluvialis cells containing different levels of AXT were mixed and underwent RADS. Those AXT-hyperproducing cells were sorted with an accuracy of 98.3%, an enrichment ratio of eight folds, and a throughput of ∼260 cells/min. Of the RADS-sorted cells, 92.7% remained alive and able to proliferate, which is equivalent to the unsorted cells. Thus, the RADS achieves a much higher throughput than existing RACS systems, preserves the vitality of cells, and facilitates seamless coupling with downstream manipulations such as single-cell sequencing and cultivation.

A PDMS-Agar Hybrid Microfluidic Device for the Investigation of Chemical-Mechanical Associative Learning Behavior of C. elegans.

Associative learning is a critical survival trait that promotes behavioral plasticity in response to changing environments. Chemosensation and mechanosensation are important sensory modalities that enable animals to gather information about their internal state and external environment. However, there is a limited amount of research on these two modalities. In this paper, a novel PDMS-agar hybrid microfluidic device is proposed for training and analyzing chemical-mechanical associative learning behavior in the nematode Caenorhabditis elegans. The microfluidic device consisted of a bottom agar gel layer and an upper PDMS layer. A chemical concentration gradient was generated on the agar gel layer, and the PDMS layer served to mimic mechanical stimuli. Based on this platform, C. elegans can perform chemical-mechanical associative learning behavior after training. Our findings indicated that the aversive component of training is the primary driver of the observed associative learning behavior. In addition, the results indicated that the neurotransmitter octopamine is involved in regulating this associative learning behavior via the SER-6 receptor. Thus, the microfluidic device provides a highly efficient platform for studying the associative learning behavior of C. elegans, and it may be applied in mutant screening and drug testing.

Optical-based microbubble for on-demand droplet release from static droplet array (SDA) for dispensing one droplet into one tube.

Static droplet array (SDA) is a pivotal tool for high-capacity screening assays, yet extraction and collection the target droplets that contain unique analytes or cells from the SDA remains one major technical bottleneck that limits its broader application. Here we present an optical-based on-demand droplet release (OODR) system by incorporating a 1064 nm laser-responsive indium tin oxide (ITO) layer into a chamber array-based droplet microfluidic chip. By focusing the 1064 nm laser onto the ITO layer, microbubbles can be created via local heating to selectively push-out the droplets from the chamber. Then the released droplet is readily exported in a one-droplet-one-tube (ODOT) manner by the inherent capillary force into pipette tip. Releasing of the droplets containing fluorescein sodium demonstrated ∼100% successful rate (9 out of 6400 droplets were successfully released) and low residual (only ∼5% of the droplet volume remains in the chamber). White or fluorescence image-based releasing of single-cell-droplets directly after cell loading or multi-cells-droplets derived from on-chip single-cell cultivation for both E. coli and yeast cells further demonstrated the wide applicability of OODR. The present system is user-friendly and has the potential to be applied in various high-throughput screening assays, including single molecule/cell analysis, drug screening, and phenotype-based cell sorting.

N7-methylguanosin regulators-mediated methylation modification patterns and characterization of the immune microenvironment in lower-grade glioma.

N7-methylguanosine (m7G) modification signature has recently emerged as a crucial regulator of tumor progression and treatment in cancer. However, there is limited information available on the genomic profile of lower-grade gliomas (LGGs) related to m7G methylation modification genes' function in tumorigenesis and progression. In this study, we employed bioinformatics methods to characterize m7G modifications in individuals with LGG from The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). We used gene set enrichment analysis (GSEA), single sample GSEA (ssGSEA), CIBERSORT algorithm, ESTIMATE algorithm, and TIDE to evaluate the association between m7G modification patterns, tumor microenvironment (TME) cell infiltration properties, and immune infiltration markers. The m7G scoring scheme using principal component analysis (PCA) was employed to investigate the m7G modification patterns quantitatively. We examined the m7G modification hub genes' expression levels in normal samples, refractory epilepsy samples, and LGG samples using immunohistochemistry, western-blotting, and qRT-PCR. Our findings revealed that individuals with LGG could be categorized into two groups based on m7G scores (high and low) according to the properties of m7G. Moreover, we observed that high m7G score was associated with significant clinical benefit and prolonged survival duration in the anti-PD-1 cohort, while low m7G score was associated with improved prognostic outcomes and increased likelihood of complete or partial response in the anti-PD-L1 cohort. Different m7G subtypes also showed varying Tumor Mutational Burden (TMB) and immune profiles and might have distinct responses to immunotherapy. Furthermore, we identified five potential genetic markers that were highly correlated with the m7G score signature index. These findings provide insight into the features and classification associated with m7G methylation modifications and may aid in improving the clinical outcome of LGG.

Robust Spontaneous Raman Flow Cytometry for Single-Cell Metabolic Phenome Profiling via pDEP-DLD-RFC.

A full-spectrum spontaneous single-cell Raman spectrum (fs-SCRS) captures the metabolic phenome for a given cellular state of the cell in a label-free, landscape-like manner. Herein a positive dielectrophoresis induced deterministic lateral displacement-based Raman flow cytometry (pDEP-DLD-RFC) is established. This robust flow cytometry platform utilizes a periodical positive dielectrophoresis induced deterministic lateral displacement (pDEP-DLD) force that is exerted to focus and trap fast-moving single cells in a wide channel, which enables efficient fs-SCRS acquisition and extended stable running time. It automatically produces deeply sampled, heterogeneity-resolved, and highly reproducible ramanomes for isogenic cell populations of yeast, microalgae, bacteria, and human cancers, which support biosynthetic process dissection, antimicrobial susceptibility profiling, and cell-type classification. Moreover, when coupled with intra-ramanome correlation analysis, it reveals state- and cell-type-specific metabolic heterogeneity and metabolite-conversion networks. The throughput of ≈30-2700 events min-1 for profiling both nonresonance and resonance marker bands in a fs-SCRS, plus the >5 h stable running time, represent the highest performance among reported spontaneous Raman flow cytometry (RFC) systems. Therefore, pDEP-DLD-RFC is a valuable new tool for label-free, noninvasive, and high-throughput profiling of single-cell metabolic phenomes.

Genomic Profiling of Lower-Grade Gliomas Subtype with Distinct Molecular and Clinicopathologic Characteristics via Altered DNA-Damage Repair Features.

Lower WHO grade II and III gliomas (LGGs) exhibit significant genetic and transcriptional heterogeneity, and the heterogeneity of DNA damage repair (DDR) and its relationship to tumor biology, transcriptome, and tumor microenvironment (TME) remains poorly understood. In this study, we conducted multi-omics data integration to investigate DDR alterations in LGG. Based on clinical parameters and molecular characteristics, LGG patients were categorized into distinct DDR subtypes, namely, DDR-activated and DDR-suppressed subtypes. We compared gene mutation, immune spectrum, and immune cell infiltration between the two subtypes. DDR scores were generated to classify LGG patients based on DDR subtype features, and the results were validated using a multi-layer data cohort. We found that DDR activation was associated with poorer overall survival and that clinicopathological features of advanced age and higher grade were more common in the DDR-activated subtype. DDR-suppressed subtypes exhibited more frequent mutations in IDH1. In addition, we observed significant upregulation of activated immune cells in the DDR-activated subgroup, which suggests that immune cell infiltration significantly influences tumor progression and immunotherapeutic responses. Furthermore, we constructed a DDR signature for LGG using six DDR genes, which allowed for the division of patients into low- and high-risk groups. Quantitative real-time PCR results showed that CDK1, CDK2, TYMS, SMC4, and WEE1 were significantly upregulated in LGG samples compared to normal brain tissue samples. Overall, our study sheds light on DDR heterogeneity in LGG and provides insight into the molecular pathways of DDR involved in LGG development.

[Dynamic change of IL-10 and TGF-beta1 in the liver of Echinococcus multilocularis-infected mice].

OBJECTIVE: To observe the dynamic expression and function of IL-10 and TGF-beta1 in liver of BALB/c mice infected with Echinococcus multilocularis (Em). METHODS: Sixty female BALB/c mice were randomly divided into experiment group and control group. Mice in the experiment group were each injected with 0.2 ml Em protoscolex suspension (containing about 400 protoscoleces) , while those in control group received same volume of normal saline. At 2, 8, 30, 90, 180, and 360 d after infection, 5 mice from each group were sacrificed and liver specimens were collected for pathological examination and immunohistochemical detection for IL-10 and TGF-beta1. RESULTS: In mice of the experiment group, Em cysts in different sizes were found in the abdominal cavity and the liver tissue, which gradually enlarged with the time. HE staining showed infiltration of lymphocytes in liver tissue, pathological change between the cyst wall and hepatic cells. In the control, the liver lobules showed integrity and inflammatory cells were seen occasionally. The level of IL-10 expression in liver tissue of the infected mice increased with the time, and reached a peak [(1639 +/- 1.73) %] at 90 d post-infection and maintained a high level thereafter. The expression of TGF-beta1 also reached the highest level [(23.69 +/- 2.29) %] . Both were significantly higher than the control (P < 0.01), though a low level expression was found in the control at 90d post-injection. CONCLUSION: The expressions of IL-10 and TGF-beta1 both increase in the middle and late stages of the infection. Besides, their inhibited functions do not be helpful for clearing and controlling Echinococcus multilocularis infection in livers.

Optimization in Chemical Modification of Single-Stranded siRNA Encapsulated by Neutral Cytidinyl/Cationic Lipids.

Single-stranded siRNA (ss-siRNA) refers to the antisense strand of siRNA, which plays the role of gene silencing. Since single-stranded RNA is unstable, the present study employed a homemade neutral cytidinyl/cationic lipid delivery system and chemical modifications to improve its stability. The results showed that with the aid of mixed lipids, ss-siRNA could knock down 40% of target mRNA at 25 nM. With 2'-F/2'-OMe, phosphorothioate and 5'-terminal phosphorylation, the optimized ss-siRNA could knock down 80% of target mRNA at 25 nM. Further knocking down AGO2, the ss-siRNAs could not effectively silence target mRNAs. Analysis of the biodistribution in vivo showed that ss-siRNA was less durable than siRNA, but spread more quickly to tissues. This study provides a safe and efficient ss-siRNA delivery and modification strategy, which lays the foundation for future works.

Arsenic Monolayers Formed by Zero-Dimensional Tetrahedral Clusters and One-Dimensional Armchair Nanochains.

One-dimensional (1D) arsenene nanostructures are predicted to host a variety of interesting physical properties including antiferromagnetic, semiconductor-semimetal transition and quantum spin Hall effect, which thus holds great promise for next-generation electronic and spintronic devices. Herein, we devised a surface template strategy in a combination with surface-catalyzed decomposition of molecular As4 cluster toward the synthesis of the superlattice of ultranarrow armchair arsenic nanochains in a large domain on Au(111). In the low annealing temperature window, zero-dimensional As4 nanoclusters are assembled into continuous films through intermolecular van der Waals and molecule-substrate interactions. At the elevated temperature, the subsequent surface-assisted decomposition of molecular As4 nanoclusters leads to the formation of a periodic array of 1D armchair arsenic nanochains that form a (2 × 3) superstructure on the Au(111) surface. These ultranarrow armchair arsenic nanochains are predicted to have a small bandgap of ∼0.50 eV, in contrast to metallic zigzag chains. In addition, the Au-supported arsenic nanochains can be flipped to form a bilayer structure through tip indentation and manipulation, suggesting the possible transfer of these nanochains from the substrate. The successful realization of arsenic nanostructures is expected to advance low-dimensional physics and infrared optoelectronic nanodevices.

Artificial intelligence-assisted automatic and index-based microbial single-cell sorting system for One-Cell-One-Tube.

Identification, sorting, and sequencing of individual cells directly from in situ samples have great potential for in-depth analysis of the structure and function of microbiomes. In this work, based on an artificial intelligence (AI)-assisted object detection model for cell phenotype screening and a cross-interface contact method for single-cell exporting, we developed an automatic and index-based system called EasySort AUTO, where individual microbial cells are sorted and then packaged in a microdroplet and automatically exported in a precisely indexed, "One-Cell-One-Tube" manner. The target cell is automatically identified based on an AI-assisted object detection model and then mobilized via an optical tweezer for sorting. Then, a cross-interface contact microfluidic printing method that we developed enables the automated transfer of cells from the chip to the tube, which leads to coupling with subsequent single-cell culture or sequencing. The efficiency of the system for single-cell printing is >93%. The throughput of the system for single-cell printing is ~120 cells/h. Moreover, >80% of single cells of both yeast and Escherichia coli are culturable, suggesting the superior preservation of cell viability during sorting. Finally, AI-assisted object detection supports automated sorting of target cells with high accuracy from mixed yeast samples, which was validated by downstream single-cell proliferation assays. The automation, index maintenance, and vitality preservation of EasySort AUTO suggest its excellent application potential for single-cell sorting.

Identification and validation of an individualized prognostic signature of lower-grade glioma based on nine immune related long non-coding RNA.

BACKGROUND: Low-grade glioma (LGG)is one of the most common and aggressive neurological malignant tumors of the central nervous system. Mounting evidence indicates that aberrantly expressed long non-coding RNA (lncRNAs) and immune cell infiltration influence low-grade glioma development. Despite the increasing amount of research on lncRNA, there are very few immune-related lncRNA for LGG studies. METHODS: We evaluated immune cell infiltration in 529 low-grade glioma patient specimens from TCGA and 1152 normal brain tissue samples from GTEx. ssGSEA was used to generate high, medium, and low immune cell infiltration groups and to examine the heterogeneity of the low-grade glioma immune microenvironment. A risk model of immune-related lncRNAs based on immune gene sets was developed. Sequential single-factor Cox regression, Lasso regression, and stepwise multiple Cox regression analyses uncovered immune-related lncRNAs with low-grade glioma prognostic value. Kaplan-Meier analysis, ROC analysis, and nomograms were used to predict low-grade glioma OS. At length, We performed GO term and KEGG enrichment analyses and used standardized enrichment scores (NES) to identify signaling pathways that were significantly enriched. RESULTS: We identified nine immune-associated lncRNAs with low-grade glioma prognostic value (AC009283.1, AC009227.1, AL121899.1, LINC00174, LINC02166, AC018647.1, AC061961.1, NRAV, and LINC00320).These prognostic lncRNAs were used to establish prognostic markers. Kaplan-Meier Survival analysis revealed a 10-year survival rate of 22.68 % (95 % CI: 13.54-38 %] in high-risk LGG vs. 54 % (95 % CI: 39.04-74.8 %] in low-risk LGG patients. Univariate Cox regression analysis showed that the HR of risk score and 95 % CI were 1.081 and (1.060-1.102) (p < 0.001), respectively. In contrast, those from multivariate Cox regression analysis were 1.066 and (1.046-1.087) (p < 0.001). This indicated that nine LncRNAs are independent prognostic factors for patients with low-grade glioma. GSEA suggests that the identified lncRNAs influence low-grade glioma tumorigenesis and prognosis by modulating immune responses and cancer pathways. CONCLUSIONS: Our data highlight the potential prognostic value of the nine immune-related lncRNA in low-grade glioma and may open new research lines and guide low-grade glioma management.

A low-cost microfluidic platform coupled with light emitting diode for optogenetic analysis of neuronal response in C. elegans.

Optogenetic method is widely used for dissecting the neuronal function and connectivity in a specific neural circuit, which can help understanding how the animal process information and generate behavior. The nematode C. elegans has a simple but complete nervous system, making it an attractive model to study the dynamics signals of neural circuits. However, in vivo analysis on neural circuits usually rely on the complex and expensive optical equipment to allow optogenetic stimulating the neuron while recording its activities in such a freely moving animal. Hence, in this paper we reported a portable optofluidic platform that works based on optical fiber illumination and functional imaging for worm optogenetic manipulation. A light beam from LED laser pen crossing the 3D-printed optical fiber channel is used to activate the neurons specific-expressed with light sensitive proteins ChR-2. The imaging light path is perpendicular to the stimulation light, which allows activating neuron precisely and measuring cellular signals simultaneously. By using such an easy-to-assemble device, optical stimulation of the specific neurons and detection of dynamic calcium responses of other neurons could be proceeded simultaneously. Thus, the developed microfluidic platform puts forward a simple, rapid and low-cost strategy for further neural circuits studies.

Identification and validation of a novel eight mutant-derived long non-coding RNAs signature as a prognostic biomarker for genome instability in low-grade glioma.

Long non-coding RNAs (lncRNAs) comprise an integral part of the eukaryotic transcriptome. Alongside proteins, lncRNAs modulate lncRNA-based gene signatures of unstable transcripts, play a crucial role as antisense lncRNAs to control intracellular homeostasis and are implicated in tumorigenesis. However, the role of genomic instability-associated lncRNAs in low-grade gliomas (LGG) has not been fully explored. In this study, lncRNAs expression and somatic mutation profiles in low-grade glioma genome were used to identify eight novel mutant-derived genomic instability-associated lncRNAs including H19, FLG-AS1, AC091932.1, AC064875.1, AL138767.3, AC010273.2, AC131097.4 and ISX-AS1. Patients from the LGG gene mutagenome atlas were grouped into training and validation sets to test the performance of the signature. The genomic instability-associated lncRNAs signature (GILncSig) was then validated using multiple external cohorts. A total of 59 novel genomic instability-associated lncRNAs in LGG were used for least absolute shrinkage and selection operator (Lasso), single and multifactor Cox regression analysis using the training set. Furthermore, the independent predictive role of risk features in the training and validation sets were evaluated through survival analysis, receiver operating feature analysis and construction of a nomogram. Patients with IDH1 mutation status were grouped into two different risk groups based on the GILncSig score. The low-risk group showed a relatively higher rate of IDH1 mutations compared with patients in the high-risk group. Furthermore, patients in the low-risk group had better prognosis compared with patients in the high-risk group. In summary, this study reports a reliable prognostic prediction signature and provides a basis for further investigation of the role of lncRNAs on genomic instability. In addition, lncRNAs in the signature can be used as new targets for treatment of LGG.

Integrated Gene Expression and Methylation Analyses Identify DLL3 as a Biomarker for Prognosis of Malignant Glioma.

Glioma is one of the most common neurological malignancies worldwide. Delta-like ligand 3 (DLL3), an inhibitory ligand-driven activation of the Notch pathway, has been shown to be significantly associated with overall survival in patients with glioma. Therefore, the purpose of this study was to determine whether DLL3 as a biomarker in glioma is associated with patients' clinicopathological features and prognosis. We identified differences in transcriptome and promoter methylation in the Chinese Glioma Genome Atlas (CGGA) in patients with malignant glioma with shorter (less than 1 year) and longer (greater than 3 years) survival time. Further analysis of The Cancer Genome Atlas (TCGA) revealed that four genes (DLL3, TSPAN15, RTN1, PAK7) are highly associated with patient prognosis and play an indispensable role in evolution. We chose the expression level of DLL3 in glioma patients for our study. Patients were divided into groups with low and high expression of DLL3 according to the cutoff values obtained, and Kaplan-Meier and Cox analysis were used to examine the correlation between DLL3 gene expression and patient survival. We then performed a gene set enrichment analysis (GSEA) to identify significantly enriched signaling pathways. Our results confirmed that the overall survival of patients with low DLL3 expression was significantly shorter than that of patients with high DLL3 expression. GSEA showed that the signaling pathways of the immune process and immune response, among others, were enhanced with the DLL3 low-expression phenotype. Collectively, our findings signify that DLL3 is a potent prognostic factor for glioma, which can provide a viable approach for glioma prognostic assessment and valuable insights for anti-tumor immune-targeted therapies.

Efficacy of 11C-2ß-carbomethoxy-3ß-(4-fluorophenyl) tropane positron emission tomography combined with 18F-fluorodeoxyglucose positron emission tomography in the diagnosis of early Parkinson disease: A protocol for systematic review and meta analysis.

BACKGROUND: Parkinson's disease (PD) has a high incidence in the elderly, and the late stage seriously affects the daily life of the patients. Most of the initial symptoms of PD are not obvious or atypical, which brings difficulties to the early diagnosis. Replacement therapy and neuroprotection after early diagnosis can significantly improve the prognosis and quality of life of patients. More and more evidence shows that 11C-2ß-carbomethoxy-3ß-(4-fluorophenyl) tropane positron emission tomography ( 11C-CFT PET) combined with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) can effectively improve the accuracy of early diagnosis. However, there is no consistent conclusion at present. The purpose of this study is to evaluate the efficacy of 11C-CFT PET combined with 18F-FDG PET in the diagnosis of early PD. METHODS: We will search 7 electronic databases (PubMed, EMBASE, Web of Science, Cochrane library, PsycINFO, AMED, Scopus), ongoing trials and grey literature to collect related randomized controlled trials and will use Review Manager Software 5.2 and STATA Software 16.0 for analysis and synthesis. RESULTS: We will integrate the existing randomized controlled trials to evaluate the value of 11C-CFT PET combined with 18F-FDG PET in the diagnosis of early PD. CONCLUSION: Our study may prove that 11C-CFT PET combined with 18F-FDG PET can effectively diagnose early PD. REGISTRATION NUMBER: International Prospective Register of Systematic Reviews (PROSPERO): CRD42020203442.

A dual-stimulation strategy in a micro-chip for the investigation of mechanical associative learning behavior of C. elegans.

During the past decades, few micro-devices for analysis of associative learning behavior have been reported. In this work, an agarose-PDMS hybridized micro-chip was developed to establish a new associative learning model between mechanosensation and food reward in C. elegans. The micro-chip consisted of column arrays which mimicked mechanical stimulation to C. elegans. After trained by pairing bacterial food and mechanical stimuli in the chip, the worms exhibited associative learning behavior and gathered in the regions where there was food during training. The key research findings include: (1) Associative learning behavior of C. elegans could be generated and quantitatively analyzed by this developed micro-chip. (2) Associative learning behavior could be enhanced by extending the training time and developmental stage. (3) Mechanosensation-related genes and neurotransmitters signals had effects on the learning behavior. (4) The associative learning ability could be strengthened by exogenous dopamine in both wild type and mutants. We validated that the design of the micro-chip was useful and convenient for the study of learning behavior based on mechanosensation.