ALOX5 contributes to glioma progression by promoting 5-HETE-mediated immunosuppressive M2 polarization and PD-L1 expression of glioma-associated microglia/macrophages.

BACKGROUND: Oxylipin metabolism plays an essential role in glioma progression and immune modulation in the tumor microenvironment. Lipid metabolic reprogramming has been linked to macrophage remodeling, while the understanding of oxylipins and their catalyzed enzymes lipoxygenases in the regulation of glioma-associated microglia/macrophages (GAMs) remains largely unexplored. METHODS: To explore the pathophysiological relevance of oxylipin in human glioma, we performed Ultra-high performance liquid chromatography-MS/MS (UHPLC-MS/MS) analysis in human glioma and non-tumor brain tissues. To comprehensively investigate the role of arachidonate lipoxygenase 5 (ALOX5) in glioma, we performed in vivo bioluminescent imaging, immunofluorescence staining and flow cytometry analysis on tumors from orthotopic glioma-bearing mice. We developed an ALOX5-targeted nanobody, and tested its anti-glioma efficacy of combination therapy with α-programmed cell death protein-1 (PD-1). RESULTS: In this study, we found that ALOX5 and its oxylipin 5-hydroxyeicosatetraenoic acid (5-HETE) are upregulated in glioma, accumulating programmed death-ligand 1 (PD-L1)+ M2-GAMs and orchestrating an immunosuppressive tumor microenvironment. Mechanistically, 5-HETE derived from ALOX5-overexpressing glioma cells, promotes GAMs migration, PD-L1 expression, and M2 polarization by facilitating nuclear translocation of nuclear factor erythroid 2-related factor 2. Additionally, a nanobody targeting ALOX5 is developed that markedly suppresses 5-HETE efflux from glioma cells, attenuates M2 polarization of GAMs, and consequently ameliorates glioma progression. Furthermore, the combination therapy of the ALOX5-targeted nanobody plus α-PD-1 exhibits superior anti-glioma efficacy. CONCLUSIONS: Our findings reveal a pivotal role of the ALOX5/5-HETE axis in regulating GAMs and highlight the ALOX5-targeted nanobody as a potential therapeutic agent, which could potentiate immune checkpoint therapy for glioma.

Radiomics Analysis of CTO Plaques for Predicting Successful Guidewire Crossing Within 30 Minutes of PCI.

BACKGROUND: Coronary computed tomography angiography provides valuable information for evaluating the difficulty of chronic total occlusion (CTO) percutaneous coronary intervention. This study aimed to investigate the value of CTO plaque characteristics derived from radiomics analysis for predicting the difficulty of percutaneous coronary intervention. METHODS: Patients with CTO were retrospectively enrolled from a hospital as training and internal test sets and from the other 2 territory hospitals as external test sets. Radiomics characteristics were extracted from the CTO segment on coronary computed tomography angiography. Radiomics and combined models were developed to predict successful guidewire crossing within 30 minutes (guidewire success) of CTO percutaneous coronary intervention. Subgroup analysis was conducted to investigate the influence of potential risk factors on the radiomics model performance. RESULTS: A total of 551 patients (median, 60; interquartile range, 52.00-66.00 years, 460 men) with 565 CTO lesions were finally enrolled. In the training, internal test, and external test sets, 203 of 357, 85 of 149, and 38 of 59 CTO lesions achieved guidewire success, respectively. Six radiomics features were selected for constructing the radiomics model. In the external test set, the area under the receiver operating characteristic curve of the radiomics model was significantly higher than prior prediction models (P<0.05 for all) with the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.86, 74.58%, 81.58%, and 61.90%, respectively. The performance of the radiomics model was dependent on calcification, CTO location, adjacent branch(es), and operator caseload. CONCLUSIONS: CTO characteristics revealed by radiomics analysis can be used as effective imaging biomarkers for predicting guidewire success. However, the performance of the radiomics model depends on anatomic and operator factors.

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

Artificial intelligence-based personalized clinical decision-making for patients with localized prostate cancer: surgery versus radiotherapy.

BACKGROUND: Surgery and radiotherapy are primary nonconservative treatments for prostate cancer (PCa). However, personalizing treatment options between these treatment modalities is challenging due to unclear criteria. We developed an artificial intelligence (AI)-based model that can identify patients with localized PCa who would benefit more from either radiotherapy or surgery, thereby providing personalized clinical decision-making. MATERIAL AND METHODS: Data from consecutive patients with localized PCa who received radiotherapy or surgery with complete records of clinicopathological variables and follow-up results in 12 registries of the Surveillance, Epidemiology, and End Results database were analyzed. Patients from 7 registries were randomly assigned to training (TD) and internal validation datasets (IVD) at a 9:1 ratio. The remaining 5 registries constituted the external validation dataset (EVD). TD was divided into training-radiotherapy (TRD) and training-surgery (TSD) datasets, and IVD was divided into internal-radiotherapy (IRD) and internal-surgery (ISD) datasets. Six models for radiotherapy and surgery were trained using TRD and TSD to predict radiotherapy survival probability (RSP) and surgery survival probability (SSP), respectively. The models with the highest concordance index (C-index) on IRD and ISD were chosen to form the final treatment recommendation model (FTR). FTR recommendations were based on the higher value between RSP and SSP. Kaplan-Meier curves were generated for patients receiving recommended (consistent group) and nonrecommended treatments (inconsistent group), which were compared using the log-rank test. RESULTS: The study included 118 236 patients, categorized into TD (TRD: 44 621; TSD: 41 500), IVD (IRD: 4949; ISD: 4621), and EVD (22 545). Both radiotherapy and surgery models accurately predicted RSP and SSP (C-index: 0.735-0.787 and 0.769-0.797, respectively). The consistent group exhibited higher survival rates than the inconsistent group, particularly among patients not suitable for active surveillance (P < .001). CONCLUSION: FTR accurately identifies patients with localized PCa who would benefit more from either radiotherapy or surgery, offering clinicians an effective AI tool to make informed choices between these 2 treatments.

An evolutionary game analysis of algorithmic indirect copyright infringement from the perspective of collusion between UGC platforms and direct infringers.

User-generated content (UGC) is developing rapidly as an emerging platform form, however, the problem of indirect copyright infringement by algorithms is becoming more and more prominent, and infringement governance has become a key act in the development of UGC platforms. When infringement occurs, recommendation algorithms expand the scope and results of infringement, while platforms choose to conspire with direct infringers for their own interests, making it difficult for infringed persons to defend their rights. In order to analyse the influence of different factors in the platform ecosystem on the subject's behavioural strategies, a "platform-infringer" evolutionary game model is constructed, and numerical simulation is used to verify the correctness of the stable results. Based on the simulation results, it is concluded that the factors of uncertain revenue, punishment and reputation loss have important influence on the decision-making behaviour of the subject of infringement governance, and accordingly, the proposed measures on the publishers, platforms and the legal level of the government are conducive to the evolution of the system to the point of positive regulation and stability of rights protection, with a view to promoting the healthier and more stable development of the UGC platforms.

A Comprehensive Interaction Network Constructed Using miRNAs and mRNAs Provides New Insights into Potato Tuberization under High Temperatures.

High temperatures delay tuberization and decrease potato (Solanum tuberosum L.) yields. However, the molecular mechanisms and regulatory networks underlying tuberization under high temperatures remain largely unknown. Here, we performed the mRNA and miRNA sequencing of leaves and stems to identify genes and regulatory networks involved in tuberization under high temperatures. A total of 2804 and 5001 differentially expressed genes (DEGs) under high-temperature stress were identified in leaves and stems, respectively. These genes were significantly enriched in gene ontology terms regarding meristem development, the sucrose biosynthetic process, and response to heat. Meanwhile, 101 and 75 differentially expressed miRNAs (DEmiRNAs) were identified in leaves and stems, respectively. We constructed an interaction network between DEmiRNAs and DEGs, identifying 118 and 150 DEmiRNA-DEG pairs in leaves and stems, respectively. We found three miRNA-mRNA candidate modules involved in tuberization under high temperatures, including stu-miR8030-5p/StCPY714, stu-miR7981f-p5/StAGL8a, and stu-miR10532A/StAGL8b. Our study constructed an interaction network between miRNAs and target genes and proposes candidate miRNA-gene modules that regulate tuber formation under high temperatures. Our study provides new insights for revealing the regulatory mechanism of the high-temperature inhibition of tuberization and also provides gene resources for improving the heat tolerance in potatoes.

Best Overall Response-Associated Signature to Doxorubicin in Soft Tissue Sarcomas: A Transcriptomic Analysis from ANNOUNCE.

PURPOSE: This exploratory analysis evaluated the tumor samples of the patients treated with doxorubicin (with or without olaratumab) in a negative phase III ANNOUNCE trial to better understand the complexity of advanced soft tissue sarcomas (STS) and to potentially identify its predictive markers. EXPERIMENTAL DESIGN: RNA sequencing was performed on pretreatment tumor samples (n = 273) from the ANNOUNCE trial to evaluate response patterns and identify potential predictive treatment markers for doxorubicin. A BOR-associated signature to doxorubicin (REDSARC) was created by evaluating tumors with radiographic response versus progression. An external cohort of doxorubicin-treated patients from the Spanish Group for Research on Sarcomas (GEIS) was used for refinement and validation. RESULTS: A total of 259 samples from the trial were considered for analysis. Comparative analyses by the treatment arm did not explain the negative trial. However, there was an association between the BOR signature and histologic subtype (χ2P = 2.0e-7) and grade (P = 0.002). There were no associations between the BOR signature and gender, age, ethnicity, or stage. Applied to survival outcomes, REDSARC was also predictive for progression-free survival (PFS) and overall survival (OS). Using the GEIS cohort, a refined 25-gene signature was identified and applied to the ANNOUNCE cohort, where it was predictive of PFS and OS in leiomyosarcoma, liposarcoma, and other sarcoma subtypes, but not in undifferentiated pleomorphic sarcoma. CONCLUSIONS: The refined REDSARC signature provides a potential tool to direct the application of doxorubicin in sarcomas and other malignancies. Validation and further refinement of the signature in other potentially subtype specific prospective cohorts is recommended.

Solution Sequential Deposition Pseudo-Planar Heterojunction: An Efficient Strategy for State-of-Art Organic Solar Cells.

Organic solar cells (OSCs) are considered as a promising new generation of clean energy. Bulk heterojunction (BHJ) structure has been widely employed in the active layer of efficient OSCs. However, precise regulation of morphology in BHJ is still challenging due to the competitive coupling between crystallization and phase separation. Recently, a novel pseudo-planar heterojunction (PPHJ) structure, prepared through solution sequential deposition, has attracted much attention. It is an easy-to-prepare structure in which the phase separation structures, interfaces, and molecular packing can be separately controlled. Employing PPHJ structure, the properties of OSCs, such as power conversion efficiency, stability, transparency, flexibility, and so on, are usually better than its BHJ counterpart. Hence, a comprehensive understanding of the film-forming process, morphology control, and device performance of PPHJ structure should be considered. In terms of the representative works about PPHJ, this review first introduces the fabrication process of active layers based on PPHJ structure. Second, the widely applied morphology control methods in PPHJ structure are summarized. Then, the influences of PPHJ structure on device performance and other property are reviewed, which largely expand its application. Finally, a brief prospect and development tendency of PPHJ devices are discussed with the consideration of their challenges.

Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma.

PURPOSE: To assess the utility of combining contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics features with clinical variables in predicting the response to induction chemotherapy (IC) for primary central nervous system lymphoma (PCNSL). METHODS: A total of 131 patients with PCNSL (101 in the training set and 30 in the testing set) who had undergone contrast-enhanced MRI scans were retrospectively analyzed. Pyradiomics was utilized to extract radiomics features, and the clinical variables of the patients were gathered. Radiomics prediction models were developed using different combinations of feature selection methods and machine learning models, and the best combination was ultimately chosen. We screened clinical variables associated with treatment outcomes and developed clinical prediction models. The predictive performance of radiomics model, clinical model, and combined model, which integrates the best radiomics model and clinical characteristics, was independently assessed and compared using Receiver Operating Characteristic (ROC) curves. RESULTS: In total, we extracted 1598 features. The best radiomics model we selected as the best utilized T-test and Recursive Feature Elimination (RFE) for feature selection and logistic regression for model building. Serum Interleukin 2 Receptor (IL-2R) and Eastern Cooperative Oncology Group (ECOG) Score were utilized to develop a clinical predictive model for assessing the response to induction chemotherapy. The results of the testing set revealed that the combined prediction model (radiomics and IL-2R) achieved the highest area under the ROC curve at 0.868 (0.683, 0.967), followed by the radiomics model at 0.857 (0.681, 0.957), and the clinical prediction model (IL-2R and ECOG) at 0.618 (0.413, 0.797). The combined model was significantly more accurate than the clinical model, with an AUC of 0.868 compared to 0.618 (P < 0.05). While the radiomics model had slightly better predictive power than the clinical model, this difference was not statistically significant (AUC, 0.857 vs. 0.618, P > 0.05). CONCLUSIONS: Our prediction model, which combines radiomics signatures from CE-MRI with serum IL-2R, can effectively stratify patients with PCNSL before high-dose methotrexate (HD-MTX) -based chemotherapy.

Multiparametric MRI-Based Machine Learning Models for the Characterization of Cystic Renal Masses Compared to the Bosniak Classification, Version 2019: A Multicenter Study.

RATIONALE AND OBJECTIVE: Accurate differentiation between benign and malignant cystic renal masses (CRMs) is challenging in clinical practice. This study aimed to develop MRI-based machine learning models for differentiating between benign and malignant CRMs and compare the best-performing model with the Bosniak classification, version 2019 (BC, version 2019). METHODS: Between 2009 and 2021, consecutive surgery-proven CRM patients with renal MRI were enrolled in this multicenter study. Models were constructed to differentiate between benign and malignant CRMs using logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms, respectively. Meanwhile, two radiologists classified CRMs into I-IV categories according to the BC, version 2019 in consensus in the test set. A subgroup analysis was conducted to investigate the performance of the best-performing model in complicated CRMs (II-IV lesions in the test set). The performances of models and BC, version 2019 were evaluated using the area under the receiver operating characteristic curve (AUC). Performance was statistically compared between the best-performing model and the BC, version 2019. RESULTS: 278 and 48 patients were assigned to the training and test sets, respectively. In the test set, the AUC and accuracy of the LR model, the RF model, the SVM model, and the BC, version 2019 were 0.884 and 75.0%, 0.907 and 83.3%, 0.814 and 72.9%, and 0.893 and 81.2%, respectively. Neither the AUC nor the accuracy of the RF model that performed best were significantly different from the BC, version 2019 (P = 0.780, P = 0.065). The RF model achieved an AUC and accuracy of 0.880 and 81.0% in complicated CRMs. CONCLUSIONS: The MRI-based RF model can accurately differentiate between benign and malignant CRMs with comparable performance to the BC, version 2019, and has good performance in complicated CRMs, which may facilitate treatment decision-making and is less affected by interobserver disagreements.

Dual Function of the Third Component in Ternary Organic Solar Cells: Broaden the Spectrum and Optimize the Morphology.

Ternary organic solar cells (T-OSCs) have attracted significant attention as high-performance devices. In recent years, T-OSCs have achieved remarkable progress with power conversion efficiency (PCE) exceeding 19%. However, the introduction of the third component complicates the intermolecular interaction compared to the binary blend, resulting in poor controllability of active layer and limiting performance improvement. To address these issues, dual-functional third components have been developed that not only broaden the spectral range but also optimize morphology. In this review, the effect of the third component on expanding the absorption range of T-OSCs is first discussed. Second, the extra functions of the third component are introduced, including adjusting the crystallinity and molecular stack in active layer, regulating phase separation and purity, altering molecular orientation of the donor or acceptor. Finally, a summary of the current research progress is provided, followed by a discussion of future research directions.

Overall Survival and Exploratory Biomarker Analyses of Abemaciclib plus Trastuzumab with or without Fulvestrant versus Trastuzumab plus Chemotherapy in HR+, HER2+ Metastatic Breast Cancer Patients.

PURPOSE: The monarcHER trial has shown that abemaciclib, a cyclin-dependent kinase 4 and 6 inhibitor, combined with fulvestrant and trastuzumab, improves progression-free survival (PFS) in hormone receptor-positive (HR+), HER2-positive (HER2+) advanced breast cancer (ABC) compared with standard-of-care (SOC) chemotherapy combined with trastuzumab. We report the final overall survival (OS) analysis, updated safety and efficacy data, and exploratory biomarker results from monarcHER. PATIENTS AND METHODS: monarcHER (NCT02675231), a randomized, multicenter, open-label, phase II trial, enrolled 237 patients across Arm A (abemaciclib, trastuzumab, fulvestrant), Arm B (abemaciclib, trastuzumab), and Arm C (SOC chemotherapy, trastuzumab). Following the statistical plan, OS and PFS were estimated in all arms. RNA sequencing (RNA-seq) was performed on archival tissue. RESULTS: Median OS was 31.1 months in Arm A, 29.2 months in Arm B, and 20.7 months in Arm C [A vs. C: HR, 0.71; 95% confidence interval (CI), 0.48-1.05; nominal two-sided P value 0.086; B vs. C: HR 0.83 (95% CI, 0.57-1.23); nominal two-sided P value 0.365]. Updated PFS and safety findings were consistent with previous results. The most frequently reported treatment-emergent adverse events included diarrhea, fatigue, nausea, neutrophil count decrease, and anemia. In exploratory RNA-seq analyses, Luminal subtypes were associated with longer PFS [8.6 vs. 5.4 months (HR, 0.54; 95% CI, 0.38-0.79)] and OS [31.7 vs. 19.7 months (HR, 0.68; 95% CI, 0.46-1.00)] compared with non-Luminal. CONCLUSIONS: In this phase II trial, abemaciclib + trastuzumab ± fulvestrant numerically improved median OS in women with HR+, HER2+ ABC compared with SOC chemotherapy + trastuzumab.

Tethered Small-Molecule Acceptor Refines Hierarchical Morphology in Ternary Polymer Solar Cells: Enhanced Stability and 19% Efficiency.

Polymer solar cells (PSCs) are promising for efficient solar energy conversion, but achieving high efficiency and device longevity within a bulk-heterojunction (BHJ) structure remains a challenge. Traditional small-molecule acceptors (SMAs) in the BHJ blend show thermodynamic instability affecting the morphology. In contrast, tethered SMAs exhibit higher glass transition temperatures, mitigating these concerns. Yet, they might not integrate well with polymer donors, causing pronounced phase separation and overpurification of mixed domains. Herein, a novel ternary device is introduced that uses DY-P2EH, a tethered dimeric SMA with conjugated side-chains as host acceptor, and BTP-ec9, a monomeric SMA as secondary acceptor, which respectively possess hypomiscibility and hypermiscibility with the polymer donor PM6. This unique combination affords a parallel-connected ternary BHJ blend, leading to a hierarchical and stable morphology. The ternary device achieves a remarkable fill factor of 80.61% and an impressive power conversion efficiency of 19.09%. Furthermore, the ternary device exhibits exceptional stability, retaining over 85% of its initial efficiency even after enduring 1100 h of thermal stress at 85 °C. These findings highlight the potential advantage of tethered SMAs in the design of ternary devices with a refined hierarchical structure for more efficient and durable solar energy conversion technologies.

The Application of CT Radiomics in the Diagnosis of Vein Wall Invasion in Patients With Renal Cell Carcinoma Combined With Tumor Thrombus.

OBJECTIVE: The objective of this study was to explore the application of radiomics combined with machine learning to establish different models to assist in the diagnosis of venous wall invasion in patients with renal cell carcinoma and venous tumor thrombus and to evaluate the diagnostic efficacy. MATERIALS AND METHODS: We retrospectively reviewed the data of 169 patients in Peking University Third Hospital from March 2015 to January 21, who was diagnosed as renal mass with venous invasion. According to the intraoperative findings, 111 patients were classified to the venous wall invasion group and 58 cases in the non-invasion group. ITK-snap was used for tumor segmentation and PyRadiomics 3.0.1 package was used for feature extraction. A total of 1598 features could be extracted from each CT image. The patients were divided into training set and testing set by time. The elastic-net regression with 4-fold cross-validation was used as a dimension-reduction method. After feature selection, a support vector machines (SVM) model, a logistic regression (LR) model, and an extra trees (ET) model were established. Then the sensitivity, specificity, accuracy, and the area under the curve (AUC) were calculated to evaluate the diagnostic performance of each model on the testing set. RESULTS: Patients before September 2019 were divided into the training set, of which 88 patients were in the invasion group and 42 patients were in the non-invasion group. The others were in the testing set, of which 32 patients were in the invasion group and 16 patients were in the non-invasion group. A total of 34 radiomics features were obtained by the elastic-net regression. The SVM model had an AUC value of 0.641 (95% CI, 0.463-0.769), a sensitivity of 1.000, and a specificity of 0.062. The LR model had an AUC value of 0.769 (95% CI, 0.620-0.877), a sensitivity of 0.913, and a specificity of 0.312. The ET model had an AUC value of 0.853 (95% CI, 0.734-0.948), a sensitivity of 0.783, and a specificity of 0.812. Among the 3 models, the ET model had the best diagnostic effect, with a good balance of sensitivity and specificity. And the higher the tumor thrombus grade, the better the diagnostic efficacy of the ET model. In inferior vena cava tumor thrombus, the sensitivity, specificity, accuracy, and AUC of ET model can be improved to 0.889, 0.800, 0.857, 0.878 (95% CI, 0.745-1.000). CONCLUSION: Machine learning combined with radiomics method can effectively identify whether venous wall was invaded by tumor thrombus and has high diagnostic efficacy with an AUC of 0.853 (95% CI, 0.734-0.948).

The protective effects of sevoflurane on subarachnoid hemorrhage.

Sevoflurane has become an important volatile anesthetic in clinic and has been widely studied in recent years. Numerous studies have demonstrated the efficacy of sevoflurane in safeguarding against brain damage across various domains. For example, it has played a neuroprotective role in subarachnoid hemorrhage (SAH), traumatic brain injury, and ischemia/reperfusion injury. The ensuing critique will focus on the significance of sevoflurane in experimental SAH and shed light on the underlying mechanisms. The findings of the current investigation demonstrate that sevoflurane possesses neuroprotective capabilities and clarify that it effectively attenuates secondary damage resulting from SAH through anti-inflammatory and anti-apoptotic pathways. More specifically, sevoflurane is observed to mitigate arterial vasospasm, diminish microvascular thrombosis, and alleviate cerebral edema. In light of these discoveries, we maintain that sevoflurane exhibits significant promise in the management of SAH, and it merits additional investigation to facilitate its prompt clinical implementation. Therefore, a thorough understanding of the neuroprotective properties of sevoflurane is beneficial to exploring novel therapeutic solutions for SAH and providing clinicians with alternative treatment modalities.

The efficacy and safety of general anesthesia vs. conscious sedation for endovascular treatment in patients with acute ischemic stroke: a systematic review and meta-analysis.

Background: Endovascular thrombectomy (EVT) is an important treatment for patients with acute ischemic stroke (AIS). A number of studies have suggested that anesthesia type (conscious sedation vs. general anesthesia) during intra-arterial treatment for acute ischemic stroke has implications for patient outcomes. Methods: PubMed, EMBASE, Cochrane Library and clinicaltrials.gov were searched for randomized controlled trials (RCTs) that were performed to evaluate general anesthesia (GA) and conscious sedation (CS) up to May 30, 2023. Review Manager 5.3 software was used to assess the data. The risk ratio (RR) and mean difference (MD) were analyzed and calculated with a fixed effect model. Results: We pooled 930 patients from seven RCTs. We conducted a meta-analysis comparing the outcomes of GA and CS in the included trials. The rate of functional independence in the GA group was higher than that in the CS group (RR: 1.17, 95% CI: 1.00-1.35; P = 0.04; I2 = 16%). The GA group had a higher successful recanalization rate than the CS group (RR: 1.15, 95% CI: 1.08-1.22; P < 0.0001; I2 = 26%). The GA group had a higher pneumonia rate than the CS group (RR: 1.69, 95% CI: 1.22-2.34; P = 0.002; I2 = 26%). In addition, there was no significant difference between GA and CS with respect to the National Institutes of Health Stroke Scale (NIHSS) score at 24 h (P = 0.62), Modified Rankin Scale (mRS) score at 90 days (P = 0.25), intracerebral hemorrhage (P = 0.54), and mortality at 3 months (P = 0.61). Conclusion: GA demonstrated superiority over CS in achieving successful recanalization and functional independence at 3 months when performing EVT in AIS patients. However, it was also associated with a higher risk of pneumonia. Further studies, particularly those with long-term follow-ups, are necessary to identify precise strategies for selecting the appropriate anesthetic modality in EVT patients. Systematic review registration: INPLASY202370116.

The relationship between atrial fibrillation and NLRP3 inflammasome: a gut microbiota perspective.

Atrial fibrillation (AF) is a common clinical arrhythmia whose pathogenesis has not been fully elucidated, and the inflammatory response plays an important role in the development of AF. The inflammasome is an important component of innate immunity and is involved in a variety of pathophysiologic processes. The NLRP3 inflammasome is by far the best studied and validated inflammasome that recognizes multiple pathogens through pattern recognition receptors of innate immunity and mediates inflammatory responses through activation of Caspase-1. Several studies have shown that NLRP3 inflammasome activation contributes to the onset and development of AF. Ecological dysregulation of the gut microbiota has been associated with the development of AF, and some evidence suggests that gut microbiota components, functional byproducts, or metabolites may induce or exacerbate the development of AF by directly or indirectly modulating the NLRP3 inflammasome. In this review, we report on the interconnection of NLRP3 inflammasomes and gut microbiota and whether this association is related to the onset and persistence of AF. We discuss the potential value of pharmacological and dietary induction in the management of AF in the context of the association between the NLRP3 inflammasome and gut microbiota. It is hoped that this review will lead to new therapeutic targets for the future management of AF.