Lycium barbarum polysaccharide reverses drug resistance in oxaliplatin-resistant colon cancer cells by inhibiting PI3K/AKT-dependent phosphomannose isomerase.

Objective: Here, we aimed to explore the effect of LBP in combination with Oxaliplatin (OXA) on reversing drug resistance in colon cancer cells through in vitro and in vivo experiments. We also aimed to explore the possible mechanism underlying this effect. Finally, we aimed to determine potential targets of Lycium barbarum polysaccharide (LBP) in colon cancer (CC) through network pharmacology and molecular docking. Methods: The invasion ability of colon cancer cells was assessed using the invasion assay. The migration ability of these cells was assessed using the migration assay and wound healing assay. Cell cycle analysis was carried out using flow cytometry. The expression levels of phosphomannose isomerase (PMI) and ATP-binding cassette transport protein of G2 (ABCG2) proteins were determined using immunofluorescence and western blotting. The expression levels of phosphatidylinositol3-kinase (PI3K), protein kinase B (AKT), B-cell lymphoma 2 (Bcl-2), and BCL2-Associated X (Bax) were determined using western blotting. Forty BALB/c nude mice purchased from Weitong Lihua, Beijing, for the in vivo analyses. The mice were randomly divided into eight groups. They were administered HCT116 and HCT116-OXR cells to prepare colon cancer xenograft models and then treated with PBS, LBP (50 mg/kg), OXA (10 mg/kg), or LBP + OXA (50 mg/kg + 10 mg/kg). The tumor weight and volume of treated model mice were measured, and organ toxicity was evaluated using hematoxylin and eosin staining. The expression levels of PMI, ABCG2, PI3K, and AKT proteins were then assessed using immunohistochemistry. Moreover, PMI and ABCG2 expression levels were analyzed using immunofluorescence and western blotting. The active components and possible targets of LBP in colon cancer were explored using in silico analysis. GeneCards was used to identify CC targets, and an online Venn analysis tool was used to determine intersection targets between these and LBP active components. The PPI network for intersection target protein interactions and the PPI network for interactions between the intersection target proteins and PMI was built using STRING and Cytoscape. To obtain putative targets of LBP in CC, we performed GO function enrichment and KEGG pathway enrichment analyses. Results: Compared with the HCT116-OXR blank treatment group, both invasion and migration abilities of HCT116-OXR cells were inhibited in the LBP + OXA (2.5 mg/mL LBP, 10 µΜ OXA) group (p < 0.05). Cells in the LBP + OXA (2.5 mg/mL LBP, 10 µΜ OXA) group were found to arrest in the G1 phase of the cell cycle. Knockdown of PMI was found to downregulate PI3K, AKT, and Bcl-2 (p < 0.05), while it was found to upregulate Bax (p < 0.05). After treatment with L. barbarum polysaccharide, 40 colon cancer subcutaneous tumor models showed a decrease in tumor size. There was no difference in the liver index after LBP treatment (p > 0.05). However, the spleen index decreased in the OXA and LBP + OXA groups (p < 0.05), possibly as a side effect of oxaliplatin. Immunohistochemistry, immunofluorescence, and western blotting showed that LBP + OXA treatment decreased PMI and ABCG2 expression levels (p < 0.05). Moreover, immunohistochemistry showed that LBP + OXA treatment decreased the expression levels of PI3K and AKT (p < 0.05). Network pharmacology analysis revealed 45 active LBP components, including carotenoids, phenylpropanoids, quercetin, xanthophylls, and other polyphenols. It also revealed 146 therapeutic targets of LBP, including AKT, SRC, EGFR, HRAS, STAT3, and MAPK3. KEGG pathway enrichment analysis showed that the LBP target proteins were enriched in pathways, including cancer-related signaling pathways, PI3K/AKT signaling pathway, and IL-17 signaling pathways. Finally, molecular docking experiments revealed that the active LBP components bind well with ABCG2 and PMI. conclusion: Our in vitro experiments showed that PMI knockdown downregulated PI3K, AKT, and Bcl-2 and upregulated Bax. This finding confirms that PMI plays a role in drug resistance by regulating the PI3K/AKT pathway and lays a foundation to study the mechanism underlying the reversal of colon cancer cell drug resistance by the combination of LBP and OXA. Our in vivo experiments showed that LBP combined with oxaliplatin could inhibit tumor growth. LBP showed no hepatic or splenic toxicity. LBP combined with oxaliplatin could downregulate the expression levels of PMI, ABCG2, PI3K, and AKT; it may thus have positive significance for the treatment of advanced metastatic colon cancer. Our network pharmacology analysis revealed the core targets of LBP in the treatment of CC as well as the pathways they are enriched in. It further verified the results of our in vitro and in vivo experiments, showing the involvement of multi-component, multi-target, and multi-pathway synergism in the drug-reversing effect of LBP in CC. Overall, the findings of the present study provide new avenues for the future clinical treatment of CC.

A bioactive calcium silicate nanowire-containing hydrogel for organoid formation and functionalization.

Organoids, which are 3D multicellular constructs, have garnered significant attention in recent years. Existing organoid culture methods predominantly utilize natural and synthetic polymeric hydrogels. This study explored the potential of a composite hydrogel mainly consisting of calcium silicate (CS) nanowires and methacrylated gelatin (GelMA) as a substrate for organoid formation and functionalization, specifically for intestinal and liver organoids. Furthermore, the research delved into the mechanisms by which CS nanowires promote the structure formation and development of organoids. It was discovered that CS nanowires can influence the stiffness of the hydrogel, thereby regulating the expression of the mechanosensory factor yes-associated protein (YAP). Additionally, the bioactive ions released by CS nanowires in the culture medium could accelerate Wnt/ß-catenin signaling, further stimulating organoid development. Moreover, bioactive ions were found to enhance the nutrient absorption and ATP metabolic activity of intestinal organoids. Overall, the CS/GelMA composite hydrogel proves to be a promising substrate for organoid formation and development. This research suggested that inorganic biomaterials hold significant potential in organoid research, offering bioactivities, biosafety, and cost-effectiveness.

Carbon Dot-Mediated Photodynamic Treatment Improves the Quality Attributes of Post-Harvest Goji Berries (Lycium barbarum L.) via Regulating the Antioxidant System.

Carbon dots (CDs) have been proposed as photosensitizers in photodynamic treatment (PDT), owing to their excellent biological attributes and budding fruit preservation applications. In the present study, CDs (4.66 nm) were synthesized for photodynamic treatment to improve the quality attributes in post-harvest goji berries. The prepared CDs extended the storage time of the post-harvest goji berries by 9 d. The CD-mediated PDT postponed the hardness and decay index loss, reduced the formation of malondialdehyde (MDA), hydrogen peroxide (H2O2), and superoxide anion (O2•-) significantly, and delayed the loss of vital nutrients like the total protein, phenols, and flavonoids. The CD-mediated PDT improved the catalase (CAT), ascorbate peroxidase (APX), peroxidase (POD), phenylalanine ammonia-lyase (PAL), glutathione reductase (GR), and superoxide dismutase (SOD) activities, but did not improve polyphenol oxidase (PPO) activity. In addition, The CD-mediated PDT induced the accumulation of ascorbic acid (ASA) and glutathione (GSH). Overall, a CD-mediated PDT could extend the storage time and augment the quality attributes in post-harvest fresh goji berries by regulating the antioxidant system.

EGST: Enhanced Geometric Structure Transformer for Point Cloud Registration.

We explore the effect of geometric structure descriptors on extracting reliable correspondences and obtaining accurate registration for point cloud registration. The point cloud registration task involves the estimation of rigid transformation motion in unorganized point cloud, hence it is crucial to capture the contextual features of the geometric structure in point cloud. Recent coordinates-only methods ignore numerous geometric information in the point cloud which weaken ability to express the global context. We propose Enhanced Geometric Structure Transformer to learn enhanced contextual features of the geometric structure in point cloud and model the structure consistency between point clouds for extracting reliable correspondences, which encodes three explicit enhanced geometric structures and provides significant cues for point cloud registration. More importantly, we report empirical results that Enhanced Geometric Structure Transformer can learn meaningful geometric structure features using none of the following: (i) explicit positional embeddings, (ii) additional feature exchange module such as cross-attention, which can simplify network structure compared with plain Transformer. Extensive experiments on the synthetic dataset and real-world datasets illustrate that our method can achieve competitive results.

Explore the Influence of Shallow Information on Point Cloud Registration.

Feature extraction is a key step for deep-learning-based point cloud registration. In the correspondence-free point cloud registration task, the previous work commonly aggregates deep information for global feature extraction and numerous shallow information which is positive to point cloud registration will be ignored with the deepening of the neural network. Shallow information tends to represent the structural information of the point cloud, while deep information tends to represent the semantic information of the point cloud. In addition, fusing information of different dimensions is conducive to making full use of shallow information. Inspired by this, we verify shallow information in the middle layers can bring a positive impact on the point cloud registration task. We design various architectures to combine shallow information and deep information to extract global features for point cloud registration. Experimental results on the ModelNet40 dataset illustrate that feature extractors that incorporate shallow information will bring positive performance.

Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins Are Detected by Single-Cell Proteomics.

Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than single-cell transcriptomics. However, most work to date has focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlation between the translational levels of a pair of proteins in a single mammalian cell. In measuring pairwise correlations among ∼1000 proteins in a population of homogeneous K562 cells under a steady-state condition, we observed multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functionally interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations into bulk samples. However, some correlations of gene or protein expression under the steady-state condition would be masked by perturbation. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs.

RORNet: Partial-to-Partial Registration Network With Reliable Overlapping Representations.

Three-dimensional point cloud registration is an important field in computer vision. Recently, due to the increasingly complex scenes and incomplete observations, many partial-overlap registration methods based on overlap estimation have been proposed. These methods heavily rely on the extracted overlapping regions with their performances greatly degraded when the overlapping region extraction underperforms. To solve this problem, we propose a partial-to-partial registration network (RORNet) to find reliable overlapping representations from the partially overlapping point clouds and use these representations for registration. The idea is to select a small number of key points called reliable overlapping representations from the estimated overlapping points, reducing the side effect of overlap estimation errors on registration. Although it may filter out some inliers, the inclusion of outliers has a much bigger influence than the omission of inliers on the registration task. The RORNet is composed of overlapping points' estimation module and representations' generation module. Different from the previous methods of direct registration after extraction of overlapping areas, RORNet adds the step of extracting reliable representations before registration, where the proposed similarity matrix downsampling method is used to filter out the points with low similarity and retain reliable representations, and thus reduce the side effects of overlap estimation errors on the registration. Besides, compared with previous similarity-based and score-based overlap estimation methods, we use the dual-branch structure to combine the benefits of both, which is less sensitive to noise. We perform overlap estimation experiments and registration experiments on the ModelNet40 dataset, outdoor large scene dataset KITTI, and natural data Stanford Bunny dataset. The experimental results demonstrate that our method is superior to other partial registration methods. Our code is available at https://github.com/superYuezhang/RORNet.

Beijing Children's Hospital guidelines on the design and conduction of the first standardized database for medulloblastoma.

Medulloblastoma (MB) is one of the most common malignant childhood brain tumors (WHO grade IV). Its high degree of malignancy leads to an unsatisfactory prognosis, requiring more precise and personalized treatment in the near future. Multi-omics and artificial intelligence have been playing a significant role in precise medical research, but their implementation needs a large amount of clinical information and biomaterials. For these reasons, it is urgent for current MB researchers to establish a large sample-size database of MB that contains complete clinical data and sufficient biomaterials such as blood, cerebrospinal fluid (CSF), cancer tissue, and urine. Unfortunately, there are few biobanks of pediatric central nervous system (CNS) tumors throughout the world for limited specimens, scarce funds, different standards collecting methods and et cl. Even though, China falls behind western countries in this area. The present research set up a standard workflow to construct the Beijing Children's Hospital Medulloblastoma (BCH-MB) biobank. Clinical data from children with MB and for collecting and storing biomaterials, along with regular follow-up has been collected and recorded in this database. In the future, the BCH-MB biobank could make it possible to validate the promising biomarkers already identified, discover unrevealed MB biomarkers, develop novel therapies, and establish personalized prognostic models for children with MB upon the support of its sufficient data and biomaterials, laying the foundation for individualized therapies of children with MB.

An Adaptive Migration Collaborative Network for Multimodal Image Classification.

The multispectral (MS) and the panchromatic (PAN) images belong to different modalities with specific advantageous properties. Therefore, there is a large representation gap between them. Moreover, the features extracted independently by the two branches belong to different feature spaces, which is not conducive to the subsequent collaborative classification. At the same time, different layers also have different representation capabilities for objects with large size differences. In order to dynamically and adaptively transfer the dominant attributes, reduce the gap between them, find the best shared layer representation, and fuse the features of different representation capabilities, this article proposes an adaptive migration collaborative network (AMC-Net) for multimodal remote-sensing (RS) images classification. First, for the input of the network, we combine principal component analysis (PCA) and nonsubsampled contourlet transformation (NSCT) to migrate the advantageous attributes of the PAN and the MS images to each other. This not only improves the quality of images themselves, but also increases the similarity between the two images, thereby reducing the representational gap between them and the pressure on the subsequent classification network. Second, for the interaction on the feature migrate branch, we design a feature progressive migration fusion unit (FPMF-Unit) based on the adaptive cross-stitch unit of correlation coefficient analysis (CCA), which can make the network automatically learn the features that need to be shared and migrated, aiming to find the best shared-layer representation for multifeature learning. And we design an adaptive layer fusion mechanism module (ALFM-Module), which can adaptively fuse features of different layers, aiming to clearly model the dependencies among multiple layers for different sized objects. Finally, for the output of the network, we add the calculation of the correlation coefficient to the loss function, which can make the network converge to the global optimum as much as possible. The experimental results indicate that AMC-Net can achieve competitive performance. And the code for the network framework is available at: https://github.com/ru-willow/A-AFM-ResNet.

Epidemiological characteristics, clinical presentations, and prognoses of pediatric brain tumors: Experiences of national center for children's health.

Background: We aimed to describe the epidemiological characteristics, clinical presentations, and prognoses in a national health center for children. Methods: From January 2015 to December 2020, 484 patients aged 0-16 years, who were diagnosed with brain tumors and received neurosurgery treatment, were enrolled in the study. Pathology was based on the World Health Organization 2021 nervous system tumor classification, and tumor behaviors were classified according to the International Classification of Diseases for Oncology, third edition. Results: Among the 484 patients with brain tumors, the median age at diagnosis was 4.62 [2.19, 8.17] years (benign tumors 4.07 [1.64, 7.13] vs. malignant tumors 5.36 [2.78, 8.84], p=0.008). The overall male-to-female ratio was 1.33:1(benign 1.09:1 vs. malignant 1.62:1, p=0.029). Nausea, vomiting, and headache were the most frequent initial symptoms. The three most frequent tumor types were embryonal tumors (ET, 22.8%), circumscribed astrocytic gliomas (20.0%), and pediatric-type diffuse gliomas (11.0%). The most common tumor locations were the cerebellum and fourth ventricle (38.67%), the sellar region (22.9%) and ventricles (10.6%). Males took up a higher proportion than females in choroid plexus tumors (63.6%), ET (61.1%), ependymal tumors (68.6%), and germ cell tumors (GCTs, 78.1%). Patients were followed for 1 to 82 months. The overall 5-year survival rate was 77.5%, with survival rates of 91.0% for benign tumors and 64.6% for malignant tumors. Conclusion: Brain tumors presented particularly sex-, age-, and regional-dependent epidemiological characteristics. Our results were consistent with previous reports and might reflect the real epidemiological status in China.

A Multi-Scale Progressive Collaborative Attention Network for Remote Sensing Fusion Classification.

With the development of remote sensing technology, panchromatic images (PANs) and multispectral images (MSs) can be easily obtained. PAN has higher spatial resolution, while MS has more spectral information. So how to use the two kinds of images' characteristics to design a network has become a hot research field. In this article, a multi-scale progressive collaborative attention network (MPCA-Net) is proposed for PAN and MS's fusion classification. Compared to the traditional multi-scale convolution operations, we adopt an adaptive dilation rate selection strategy (ADR-SS) to adaptively select the dilation rate to deal with the problem of category area's excessive scale differences. For the traditional pixel-by-pixel sliding window sampling strategy, the patches which are generated by adjacent pixels but belonging to different categories contain a considerable overlap of information. So we change original sampling strategy and propose a center pixel migration (CPM) strategy. It migrates the center pixel to the most similar position of the neighborhood information for classification, which reduces network confusion and increases its stability. Moreover, due to the different spatial and spectral characteristics of PAN and MS, the same network structure for the two branches ignores their respective advantages. For a certain branch, as the network deepens, characteristic has different representations in different stages, so using the same module in multiple feature extraction stages is inappropriate. Thus we carefully design different modules for each feature extraction stage of the two branches. Between the two branches, because the strong mapping methods of directly cascading their features are too rough, we design collaborative progressive fusion modules to eliminate the differences. The experimental results verify that our proposed method can achieve competitive performance.

RDLNet: A Regularized Descriptor Learning Network.

Local image descriptor learning has been instrumental in various computer vision tasks. Recent innovations lie with similarity measurement of descriptor vectors with metric learning for randomly selected Siamese or triplet patches. Local image descriptor learning focuses more on hard samples since easy samples do not contribute much to optimization. However, few studies focus on hard samples of image patches from the perspective of loss functions and design appropriate learning algorithms to obtain a more compact descriptor representation. This article proposes a regularized descriptor learning network (RDLNet) that makes the network focus on the learning of hard samples and compact descriptor with triplet networks. A novel hard sample mining strategy is designed to select the hardest negative samples in mini-batch. Then batch margin loss concerned with hard samples is adopted to optimize the distance of extreme cases. Finally, for a more stable network and preventing network collapsing, orthogonal regularization is designed to constrain convolutional kernels and obtain rich deep features. RDLNet provides a compact discriminative low-dimensional representation and can be embedded in other pipelines easily. This article gives extensive experimental results for large benchmarks in multiple scenarios and generalization in matching applications with significant improvements.

Hydrogen sulfide treatment improves quality attributes via regulating the antioxidant system in goji berry (Lycium barbarum L.).

Hydrogen sulfide (H2S) has been identified as a critical gaseous signaling chemical. Herein, the effects of H2S treatment on the postharvest goji berries and antioxidant enzyme activities were determined. H2S application delayed the decay index, loss of firmness, color, flavor, and total sugars and loss of total protein, betaine and ascorbic acid in goji berries during postharvest storage. Meanwhile, H2S noticeably reduced the MDA, H2O2, and O2- accumulation. Additionally, it was shown that H2S increased the activity of catalase (CAT), ascorbate peroxidase (APX), peroxidase (POD), glutathione reductase (GR) and superoxide dismutase (SOD) while decreased the quantity of lipoxygenase (LOX). The mRNA expression of LDC, DCD, CAT, APX, POD, GR and SOD was up-regulated but LOX, RBOH-b and RBOH-e was down-regulated in goji berries after H2S treatment. Altogether, H2S could efficiently delay the senescence, improves postharvest quality, increase the bioactive compounds accumulation, and boost the antioxidant capacity of goji berries through modulating antioxidant enzyme system.

Metabolic Profiling and Molecular Mechanisms Underlying Melatonin-Induced Secondary Metabolism of Postharvest Goji Berry (Lycium barbarum L.).

Postharvest decay of goji berries, mainly caused by Alternaria alternata, results in significant economic losses. To investigate the effects of melatonin (MLT) on resistance to Alternaria rot in goji berries, the fruits were immersed in the MLT solutions with varying concentrations (0, 25, 50, and 75 µmol L-1) and then inoculated with A. alternata. The results showed that the fruits treated with 50 µmol L-1 MLT exhibited the lowest disease incidence and least lesion diameter. Meanwhile, endogenous MLT in the fruits treated with 50 µmol L-1 MLT showed higher levels than in the control fruits during storage at 4 ± 0.5 °C. Further, the enzymatic activities and expressions of genes encoding peroxidase, phenylalanine ammonia-lyase, cinnamate 4-hydroxylase, 4-coumarate-CoA ligase, chalcone synthase, chalcone isomerase, and cinnamyl alcohol dehydrogenase were induced in the treated fruit during storage. UPLC-ESI-MS/MS revealed that secondary metabolites in the fruits on day 0, in order of highest to lowest levels, were rutin, p-coumaric acid, chlorogenic acid, ferulic acid, caffeic acid, naringenin, quercetin, kaempferol, and protocatechuic acid. MLT-treated fruits exhibited higher levels of secondary metabolites than the control. In conclusion, MLT treatment contributed to controlling the postharvest decay of goji fruit during storage by boosting endogenous MLT levels, thus activating the antioxidant system and secondary metabolism.

Correlated gene modules uncovered by high-precision single-cell transcriptomics.

Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome.

WES data from 286 diffuse gliomas under the 2021 WHO Classification of Tumors of the Central Nervous System.

Diffuse gliomas (DGs) are the most common and lethal primary neoplasms in the central nervous system. The latest 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) was published in 2021, immensely changing the approach to diagnosis and decision making. As a part of the Chinese Glioma Genome Atlas (CGGA) project, our aim was to provide genomic profiling of gliomas in a Chinese cohort. Two hundred eighty six gliomas with different grades were collected over the last decade. Using the Illumina HiSeq platform, over 75.8 million high-quality 150 bp paired-end reads were generated per sample, yielding a total of 43.4 billion reads. We also collected each patient's clinical and pathological information and used it to annotate their genetic data. All patients were diagnosed and classified by neuro-pathologist under the 2021 WHO classification. This dataset provides an important reference for researchers and will significantly advance our understanding of gliomas.

Convex and Concave Model 3D Printing for Designing Right-side Bronchial Blocker for Infants.

It is technically challenging for pediatric anesthesiologists to use bronchial blocker (BB) to isolate the lungs of infants during thoracoscopic surgery. Further, BB currently sold in the market cannot match the anatomical characteristics of the infants, especially on the right main bronchus. It may easily cause poor exhaustion of the right upper lobe, which leads to interference with the thoracoscopic surgical field. The two dimensional reconstruction data of 124 normal infants' airways were extracted from the medical image database of Beijing Children's Hospital for statistical analysis. After using linear fitting and goodness-of-fit test, a good linear relationship was detected between infant age and various parameters related to aid in designing a new BB for infants (R2=0.502). According to the growth and development rate of infants, the DICOM files of airway CT scan of 7 infants aged 30, 60, 90, 120, 180, 270, and 360 days were selected to print non-transparent convex and transparent concave 3D models. The non-transparent convex model was precisely measured to obtain the important parameters for BB design infants only, to complete the design of BB, to generate the sample, and to verify the blocking effect of produced sample in transparent concave three-dimensional (3D) model.

A novel chemokine-based signature for prediction of prognosis and therapeutic response in glioma.

AIMS: Gliomas are the primary malignant brain tumor and characterized as the striking cellular heterogeneity and intricate tumor microenvironment (TME), where chemokines regulate immune cell trafficking by shaping local networks. This study aimed to construct a chemokine-based gene signature to evaluate the prognosis and therapeutic response in glioma. METHODS: In this study, 1024 patients (699 from TCGA and 325 from CGGA database) with clinicopathological information and mRNA sequencing data were enrolled. A chemokine gene signature was constructed by combining LASSO and SVM-RFE algorithm. GO, KEGG, and GSVA analyses were performed for function annotations of the chemokine signature. Candidate mRNAs were subsequently verified through qRT-PCR in an independent cohort including 28 glioma samples. Then, through immunohistochemical staining (IHC), we detected the expression of immunosuppressive markers and explore the role of this gene signature in immunotherapy for glioma. Lastly, the Genomics of Drug Sensitivity in Cancer (GDSC) were leveraged to predict the potential drug related to the gene signature in glioma. RESULTS: A constructed chemokine gene signature was significantly associated with poorer survival, especially in glioblastoma, IDH wildtype. It also played an independent prognostic factor in both datasets. Moreover, biological function annotations of the predictive signature indicated the gene signature was positively associated with immune-relevant pathways, and the immunosuppressive protein expressions (PD-L1, IBA1, TMEM119, CD68, CSF1R, and TGFB1) were enriched in the high-risk group. In an immunotherapy of glioblastoma cohort, we confirmed the chemokine signature showed a good predictor for patients' response. Lastly, we predicted twelve potential agents for glioma patients with higher riskscore. CONCLUSION: In all, our results highlighted a potential 4-chemokine signature for predicting prognosis in glioma and reflected the intricate immune landscape in glioma. It also threw light on integrating tailored risk stratification with precision therapy for glioblastoma.

Hydrogen sulfide treatment increases the antioxidant capacity of fresh Lingwu Long Jujube (Ziziphus jujuba cv. Mill) fruit during storage.

Hydrogen sulfide (H2S) has been identified as an important gaseous signal molecule in plants. Here, we investigated the effects of H2S on postharvest senescence and antioxidant metabolism of Lingwu Long Jujube (Ziziphus jujuba cv. Mill) fruits (LLJF). Fumigation of Jujube fruits with H2S released from 0.4 mm NaHS could significantly prolong the postharvest shelf life of jujube fruits, reduce the decay rate of fruit, the weight loss of fruit, and inhibit the fruit loss, hardness, color, soluble solids, and titratable acidity. Compared with the control group, exogenous H2S fumigation significantly decreased the loss of chlorophyll, carotenoids, soluble protein, ascorbic acid, phenols, and flavonoids in jujube fruits during post-harvest storage. At the same time, H2S could significantly delay the accumulation of malondialdehyde (MDA), hydrogen peroxide (H2O2) and superoxide anion (O2 ∙-) and promote catalase (CAT), superoxide dismutase (SOD), ascorbate peroxidase (APX), peroxidase (POD) activity, and inhibit polyphenol oxidase (PPO) activity. To summarize, H2S can effectively alleviate postharvest senescence and decay of jujube fruits by regulating the ROS accumulation and antioxidant enzymes, and prolong the storage period of postharvest.

Novel mutation of SIK1 gene causing a mild form of pediatric epilepsy in a Chinese patient.

Developmental and Epileptic Encephalopathy (DEE) is a group of disorders affecting children at early stages of infancy, which is characterized by frequent seizures, epileptiform activity on EEG, and developmental delayor regression. Developmental and epileptic encephalopathy-30 (DEE30) is a severe neurologic disorder characterized by onset of refractory seizures soon after birth or in the first months of life. Which was recently found to be caused by heterozygous mutations in the salt-inducible kinase SIK1. In this study, we investigated a patient with early onset epilepsy. DNA sequencing of the whole coding region revealed a de novel heterozygous nucleotide substitution (c.880G > A) causing a missense mutation (p.A294T). This mutation was classified as variant of unknown significance (VUS) by American College of Medical Genetics and Genomics (ACMG). To further investigate the pathogenicity and pathogenesis of this mutation, we established a human neuroblastoma cell line (SH-SY5Y) stably-expressing wild type SIK1 and A294T mutant, and compared the transcriptome and metabolomics profiles. We presented a pediatric patient suffering from infantile onset epilepsy. Early EEG showed a boundary dysfunction of activity and MRI scan of the brain was normal. The patient responded well to single anti-epileptic drug treatment. Whole-exome sequencing found a missense mutation of SIK1 gene (c.880G > A chr21: 43,420,326 p. A294T). Dysregulated transcriptome and metabolome in cell models expressing WT and MUT SIK1 confirmed the pathogenicity of the mutation. Specifically, we found MEF2C target genes, certain epilepsy causing genes and metabolites are dysregulated by SIK1 mutation. We found MEF2C target genes, certain epilepsy causing genes and metabolites are dysregulated by SIK1 mutation. Our finding further expanded the disease spectrum and provided novel mechanistic insights of DEE30.