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Introduction: Spontaneous subarachnoid hemorrhage (SAH), is a disorder that may be fatal and is primarily caused by a ruptured brain aneurysm. Despite significant leaps forward in the methods to produce aneurysms, the long-term outcomes did not much improve. Pioglitazone is a medication that has been authorized by the FDA as an agonist for the peroxisome proliferator-activated receptor-gamma (PPARγ). Pioglitazone or PPARγ has neuroprotective benefits in animal experiments both during and after traumatic brain injury (TBI) and SAH. Nevertheless, the treatment impact of Pioglitazone on humans is still unknown at this time. As a result, we will conduct a randomized, double-blind, placebo-controlled trial to explore the impact of pioglitazone on SAH.Methods/Design: This trial will recruit 400 patients with SAH from four Chinese hospitals. These patients will be equally and randomly assigned to Pioglitazone and placebo control groups for up to 30 days. Scores on the modified Rankin scale (mRS) are the primary outcomes. The secondary outcomes are a 30-day all-cause mortality rate, 6 months of Montreal cognitive assessment (Mo-CA), delayed cerebral ischemia, the requirement for intensive care, the incidence of sepsis, etc. All serious adverse events (SAEs) were recorded during the hospital. Every primary and safety analysis was conducted based on the intention-to-treat technique. The participants were given either a matching placebo or 15 mg of pioglitazone, with dose titrated to a target of 45 mg daily. Data on the therapeutic use of pioglitazone after SAH will be provided as a consequence of the findings of this experiment. In addition, this pilot trial is the first to prospectively investigate the effectiveness and safety of pioglitazone in patients with SAH.Ethics and dissemination: Ethics approval was obtained from the Medical Ethics Committee of 904th Hospital of Joint Logistic Support Force of PLA (Wuxi Taihu Hospital, approval No. 20220701). The findings of the trial will be presented at conferences, discussed in relevant patient groups, and published in peer-reviewed journals.Clinical Trial Registration:clinicaltrials.gov, identifier ChiCTR2200062954.

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993–0.999) and segmented (Dice scores 0.550–0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815–0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

Background2021 World Health Organization (WHO) Central Nervous System (CNS) tumor classification increasingly emphasizes the important role of molecular markers in glioma diagnoses. Preoperatively non-invasive “integrated diagnosis” will bring great benefits to the treatment and prognosis of these patients with special tumor locations that cannot receive craniotomy or needle biopsy. Magnetic resonance imaging (MRI) radiomics and liquid biopsy (LB) have great potential for non-invasive diagnosis of molecular markers and grading since they are both easy to perform. This study aims to build a novel multi-task deep learning (DL) radiomic model to achieve preoperative non-invasive “integrated diagnosis” of glioma based on the 2021 WHO-CNS classification and explore whether the DL model with LB parameters can improve the performance of glioma diagnosis.MethodsThis is a double-center, ambispective, diagnostical observational study. One public database named the 2019 Brain Tumor Segmentation challenge dataset (BraTS) and two original datasets, including the Second Affiliated Hospital of Nanchang University, and Renmin Hospital of Wuhan University, will be used to develop the multi-task DL radiomic model. As one of the LB techniques, circulating tumor cell (CTC) parameters will be additionally applied in the DL radiomic model for assisting the “integrated diagnosis” of glioma. The segmentation model will be evaluated with the Dice index, and the performance of the DL model for WHO grading and all molecular subtype will be evaluated with the indicators of accuracy, precision, and recall.DiscussionSimply relying on radiomics features to find the correlation with the molecular subtypes of gliomas can no longer meet the need for “precisely integrated prediction.” CTC features are a promising biomarker that may provide new directions in the exploration of “precision integrated prediction” based on the radiomics, and this is the first original study that combination of radiomics and LB technology for glioma diagnosis. We firmly believe that this innovative work will surely lay a good foundation for the “precisely integrated prediction” of glioma and point out further directions for future research.Clinical trail registrationThis study was registered on ClinicalTrails.gov on 09/10/2022 with Identifier NCT05536024.

Abstract Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor. The unregulated expression of Claudin-4 (CLDN4) plays an important role in tumor progression. However, the biological role of CLDN4 in GBM is still unknown. This study aimed to determine whether CLDN4 mediates glioma malignant progression, if so, it would further explore the molecular mechanisms of carcinogenesis. Our results revealed that CLDN4 was significantly upregulated in glioma specimens and cells. The inhibition of CLND4 expression could inhibit mesenchymal transformation, cell invasion, cell migration and tumor growth in vitro and in vivo. Moreover, combined with in vitro analysis, we found that CLDN4 can modulate tumor necrosis factor-α (TNF-α) signal pathway. Meanwhile, we also validated that the transforming growth factor-β (TGF-β) signal pathway can upregulate the expression of CLDN4, and promote the invasion ability of GBM cells. Conversely, TGF-β signal pathway inhibitor ITD-1 can downregulate the expression of CLDN4, and inhibit the invasion ability of GBM cells. Furthermore, we found that TGF-β can promote the nuclear translocation of CLDN4. In summary, our findings indicated that the TGF-β/CLDN4/TNF-α/NF-κB signal axis plays a key role in the biological progression of glioma. Disrupting the function of this signal axis may represent a new treatment strategy for patients with GBM.

Glioblastoma multiforme (GBM) is the most common and most fatal primary malignant brain tumour in adults. The average survival time of patients after diagnosis is only 12–15 months. And its characteristics of excessive proliferation and apoptosis evasion play a crucial role in the poor prognosis of patients. Therefore, it is worth investigating the molecular mechanism of GBM to find an effective therapeutic target to overcome the dilemma. In the current study, Transmembrane BAX inhibitor motif containing 1 (TMBIM1) was highly expressed in GBM tissues and high TMBIM1 expression in GBM cell lines (U87 and U251) could promote cell proliferation and inhibit cell cycle arrest. In addition, TMBIM1 could significantly attenuate GBM cell apoptosis and decrease the sensitivity of GBM cells to temozolomide (TMZ). In terms of the molecular mechanism, we revealed that TMBIM1 interferes with the p38/MAPK pathway by inhibiting p38 phosphorylation to promote cell proliferation and attenuate cell apoptosis. In vivo experiments showed that the survival time of mice in TMBIM1 knockdown group was significantly prolonged. Our discovery provided an important basis for future intensive molecular mechanism research in GBM and presented a potential target for the treatment of GBM.

BackgroundsAs a most widely used machine learning method, tree-based algorithms have not been applied to predict delayed cerebral ischemia (DCI) in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH). Hence, this study aims to develop the conventional regression and tree-based models and determine which model has better prediction performance for DCI development in hospitalized elderly patients after aSAH.MethodsThis was a multicenter, retrospective, observational cohort study analyzing elderly patients with aSAH aged 60 years and older. We randomly divided the multicentral data into model training and validation cohort in a ratio of 70–30%. One conventional regression and tree-based model, such as least absolute shrinkage and selection operator (LASSO), decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost), was developed. Accuracy, sensitivity, specificity, area under the precision-recall curve (AUC-PR), and area under the receiver operating characteristic curve (AUC-ROC) with 95% CI were employed to evaluate the model prediction performance. A DeLong test was conducted to calculate the statistical differences among models. Finally, we figured the importance weight of each feature to visualize the contribution on DCI.ResultsThere were 111 and 42 patients in the model training and validation cohorts, and 53 cases developed DCI. According to AUC-ROC value in the model internal validation, DT of 0.836 (95% CI: 0.747–0.926, p = 0.15), RF of 1 (95% CI: 1–1, p < 0.05), and XGBoost of 0.931 (95% CI: 0.885–0.978, p = 0.01) outperformed LASSO of 0.793 (95% CI: 0.692–0.893). However, the LASSO scored a highest AUC-ROC value of 0.894 (95% CI: 0.8–0.989) than DT of 0.764 (95% CI: 0.6–0.928, p = 0.05), RF of 0.821 (95% CI: 0.683–0.959, p = 0.27), and XGBoost of 0.865 (95% CI: 0.751–0.979, p = 0.69) in independent external validation. Moreover, the LASSO had a highest AUC-PR value of 0.681 than DT of 0.615, RF of 0.667, and XGBoost of 0.622 in external validation. In addition, we found that CT values of subarachnoid clots, aneurysm therapy, and white blood cell counts were the most important features for DCI in elderly patients with aSAH.ConclusionsThe LASSO had a superior prediction power than tree-based models in external validation. As a result, we recommend the conventional LASSO regression model to predict DCI in elderly patients with aSAH.

The Fc Fragment of IgG Binding Protein (FCGBP) has been proven to participate in intestinal tumor immunity. However, the biological role of FCGBP has remained unclear in glioma. The differential expression of FCGBP was explored by Oncomine and GEPIA databases. The effect of FCGBP on prognosis was analyzed via Kaplan–Meier plotter and GEPIA. The Tumor Immune Estimation Resource (TIMER) tool was used to determine the correlations of FCGBP expression with tumor immune infiltration. Firstly, FCGBP was highly expressed in glioma and correlated with a worse prognosis. Gene Ontology (GO) and KEGG pathway enrichment analyses revealed that the differentially expressed genes (DEGs) and co-expression genes of FCGBP were mainly involved in the immune response. Furthermore, FCGBP expression was positively associated with multiple immune cells infiltrates as well as the expression levels of multiple immune markers in glioma. FCGBP co-expression networks mostly participated in the regulation of immune response. Finally, immunohistochemistry (IHC) assays were conducted to explore the expression of FCGBP, PD-L1, CCL2 and CD8 in glioma and correlations between them. We found that PDL1 and FCGBP were synchronously upregulated in glioma tissues. These findings revealed a new mechanism by which FCGBP participates in the immune tolerance of glioma, and implied the potential of FCGBP as a therapeutic target or predictive marker for patients.

Recent studies showed that molecule interacting with CasL2 (MICAL2) could be a novel tumor growth factor, and it is closely associated with tumor growth and invasion. However, the role it plays in glioblastoma (GBM) and its potential mechanisms are currently unknown. Our study is designed to identify the effect of MICAL2 on GBM cells and the potential mechanisms behind it. Here, we found that MICAL2 interacts with TGF receptor-type I (TGFRI) and promotes the proliferation and migration of glioblastoma through the TGF-β/p-Smad2/EMT-like signaling pathway. MICAL2-knockdown inhibited the proliferation of glioblastoma cells, which was related to cell cycle arrest and downregulation of DNA replication. The invasion abilities of U87 and U251 cells were reduced after the knockdown of MICAL2. MICAL2 promoted the growth of GBM in nude mice. High MICAL2 predicts poor outcome of GBM patients. MICAL2 could be identified as a novel promising therapeutic target for human GBM.

Background: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. Materials and methods: PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. Results: A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P= 0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P=0.001). Conclusion: This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A), a well-characterized RNA modification, is involved in regulating multiple biological processes; however, genome-wide identification and functional characterization of the m6A modification in legume plants, including soybean (Glycine max (L.) Merr.), remains lacking. In this study, we utilized bioinformatics tools to perform comprehensive analyses of molecular writer candidates associated with the RNA m6A modification in soybean, characterizing their conserved domains, motifs, gene structures, promoters, and spatial expression patterns. Thirteen m6A writer complex genes in soybean were identified, which were assigned to four families: MT-A70, WTAP, VIR, and HAKAI. It also can be identified that multiple cis elements in the promoters of these genes, which were classified into five distinct groups, including elements responsive to light, phytohormone regulation, environmental stress, development, and others, suggesting that these genes may modulate various cellular and physiological processes in plants. Importantly, the enzymatic activities of two identified m6A writers, GmMTA1 and GmMTA2, were confirmed in vitro. Furthermore, we analyzed the expression patterns of the GmMTAs and GmMTBs under different abiotic stresses, revealing their potential involvement in stress tolerance, especially in the response to alkalinity or darkness. Overexpressing GmMTA2 and GmMTB1 in soybean altered the tolerance of the plants to alkalinity and long-term darkness, further confirming their effect on the stress response. Collectively, our findings identified the RNA m6A writer candidates in leguminous plants and highlighted the potential roles of GmMTAs and GmMTBs in the response to abiotic stress in soybean.

Soybean (Glycine max (L.) Merr) is one of the most important crops worldwide, but its yield is vulnerable to abiotic stresses. In Arabidopsis, the AlkB homologue (ALKBH) family genes plays a crucial role in plant development and stress response. However, the identification and functions of its homologous genes in soybean remain obscured. Here, we identified a total of 22 ALKBH genes in soybean and classified them into seven subfamilies according to phylogenetic analysis. Gene duplication events among the family members and gene structure, conserved domains, and motifs of all candidate genes were analyzed. By comparing the changes in the m6A levels on mRNA from hair roots between soybean seedlings harboring the empty vector and those harboring the GmALKBH10B protein, we demonstrated that all four GmALKBH10B proteins are bona fide m6A RNA demethylases in vivo. Subcellular localization and expression patterns of the GmALKBH10B revealed that they might be functionally redundant. Furthermore, an analysis of cis-elements coupled with gene expression data demonstrated that GmALKBH10B subfamily genes, including GmALKBH10B1, GmALKBH10B2, GmALKBH10B3, and GmALKBH10B4, are likely involved in the cis-elements’ response to various environmental stimuli. In summary, our study is the first to report the genome-wide identification of GmALKBH family genes in soybean and to determine the function of GmALKBH10B proteins as m6A RNA demethylases, providing insights into GmALKBH10B genes in response to abiotic stresses.

Benzoxazinoids (BXs) are potent secondary metabolites that affect plants' biotic and abiotic interactions. Extensive studies on the functions of benzoxazinoids in mediating biotic and abiotic stressors have been reported in maize, wheat, and rye. However, little is known about BXs biosynthesis in rice. This study presents a genome-wide analysis of forty-three Oryzae sativa BXs genes that form diphyletic clusters in a neighbor-joining tree. The first cluster comprised genes that encode the first four steps in BXs biosynthesis (BX2–BX4), leading to the production of 2,4-dihydroxy-2H-1,4-benzoxazin-3(4H)-one (DIBOA). The second cluster mainly comprised BX6 and BX7 genes responsible for BXs glycosylation. Furthermore, BX proteins harboring similar conserved motifs were found to group according to their phylogenetic clustering. Whereas the P450 superfamily protein is conserved in BX1-BX4 proteins, the UDP-glucosyltransferase is conserved in BX7 members. These proteins were found to be strategically localized in subcellular compartments where their catalytic activities are executed. BX1 proteins are localized in the chloroplasts, where they encode the production of indole. BX6 were identified as cytoplasmic proteins, where they are involved in the hydroxylation of DIBOA-Glycoside to 2-(2,4,7-trihydroxy-8-methoxy-1,4-benzoxazine-3-one)-β-d-glucopyranose (TRIBOA-Glycoside). Further investigation of the molecular addresses showed that BX proteins tend to localize together on chromosomes based on their functions. Moreover, the expression profiles of these proteins vary at various developmental stages in rice tissues and organs, highlighting the potential for biotic and abiotic interactions. The prospects of BXs genes in growth induction were also investigated by analyzing their responsiveness to plant hormones and nutrient treatment. BX gene expression is affected by exogenous treatment with auxin and gibberellin as well as nitrogen, potassium, and phosphorus contents, suggesting a possible role in growth mediation. This study lays the foundation for further studies to elucidate the functions of BX genes in rice.

Rice, as a major staple crop, employs multiple strategies to enhance drought tolerance and subsequently increase yield. Osmotin-like proteins have been shown to promote plant resistance to biotic and abiotic stress. However, the drought resistance mechanism of osmotin-like proteins in rice remains unclear. This study identified a novel osmotin-like protein, OsOLP1, that conforms to the structure and characteristics of the osmotin family and is induced by drought and NaCl stress. CRISPR/Cas9-mediated gene editing and overexpression lines were used to investigate the impact of OsOLP1 on drought tolerance in rice. Compared to wild-type plants, transgenic rice plants overexpressing OsOLP1 showed high drought tolerance with leaf water content of up to 65%, and a survival rate of 53.1% by regulating 96% stomatal closure and more than 2.5-fold proline content promotion through the accumulation of 1.5-fold endogenous ABA, and enhancing about 50% lignin synthesis. However, OsOLP1 knockout lines showed severely reduced ABA content, decreased lignin deposition, and weakened drought tolerance. In conclusion, the finding confirmed that OsOLP1 drought-stress modulation relies on ABA accumulation, stomatal regulation, proline, and lignin accumulation. These results provide new insights into our perspective on rice drought tolerance.

Magnaporthe oryzae is a potent fungus that adversely affects rice yield. Combinatorial techniques of prevention, toxic chemicals, and fungicide are used to remedy rice blast infection. We reported the role of Hrip1 in cell death elicitation and expression of systematic acquired resistance that could potentially stifle M. oryzae infection. In this study, transcriptome and metabolomic techniques were used to investigate the mechanism by which Hrip1 reprogramed the transcriptome of rice seedlings to confer immunity against M. oryzae. Our results showed that Hrip1 induces cell wall thickening and phytoalexin elicitation to confer immunity against M. oryzae infection. Hrip1 activates key lignin biosynthetic genes and myeloblastosis transcription factors that act as molecular switches for lignin production. Lignin content was increased by 68.46% and more after 48 h onwards in Hrip1-treated seedlings compared to the control treatment. Further analysis of cell wall morphology using the transmission electron microscopy technique revealed over 100% cell wall robustness. Hrip1 also induced the expression of 24 diterpene synthases. These include class I and II terpene synthases, cytochrome P450 subfamilies (OsCYP76M and OsCYP71Z), and momilactones synthases. The relationship between the expression of these genes and metabolic elicitation was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Enhanced amounts of momilactones A and B, oryzalactone, and phytocassane A and G were detected in the Hrip1-treated leaves. We also identified seven benzoxazinoid genes (BX1-BX7) that could improve rice immunity. Our findings show that Hrip1 confers dual immunity by leveraging lignin and phytoalexins for physical and chemical resistance. This study provides novel insights into the mechanisms underlying Hrip1-treated plant immunity.

Fungi infections cause approximately 60–70% yield loss through diseases such as rice blast, powdery mildew, Fusarium rot, downy mildew, etc. Plants naturally respond to these infections by eliciting an array of protective metabolites to confer physical or chemical protection. Among plant metabolites, lignin, a phenolic compound, thickens the middle lamella and the secondary cell walls of plants to curtail fungi infection. The biosynthesis of monolignols (lignin monomers) is regulated by genes whose transcript abundance significantly improves plant defense against fungi. The catalytic activities of lignin biosynthetic enzymes also contribute to the accumulation of other defense compounds. Recent advances focus on modifying the lignin pathway to enhance plant growth and defense against pathogens. This review presents an overview of monolignol regulatory genes and their contributions to fungi immunity, as reported over the last five years. This review expands the frontiers in lignin pathway engineering to enhance plant defense.

Biodiversity is adversely affected by the growing levels of synthetic chemicals released into the environment due to agricultural activities. This has been the driving force for embracing sustainable agriculture. Plant secondary metabolites offer promising alternatives for protecting plants against microbes, feeding herbivores, and weeds. Terpenes are the largest among PSMs and have been extensively studied for their potential as antimicrobial, insecticidal, and weed control agents. They also attract natural enemies of pests and beneficial insects, such as pollinators and dispersers. However, most of these research findings are shelved and fail to pass beyond the laboratory and greenhouse stages. This review provides an overview of terpenes, types, biosynthesis, and their roles in protecting plants against microbial pathogens, insect pests, and weeds to rekindle the debate on using terpenes for the development of environmentally friendly biopesticides and herbicides.