Nanobodies against African swine fever virus p72 and CD2v proteins as reagents for developing two cELISAs to detect viral antibodies.

African swine fever virus (ASFV) poses a significant threat to the global swine industry. Currently, there are no effective vaccines or treatments available to combat ASFV infection in pigs. The primary means of controlling the spread of the disease is through rapid detection and subsequent elimination of infected pig. Recently, a lower virulent ASFV isolate with a deleted EP402R gene (CD2v-deleted) has been reported in China, which further complicates the control of ASFV infection the pig farms. Furthermore, an EP402R-deleted ASFV variant has been developed as a potential live attenuated vaccine candidate strain. Therefore, it is crucial to develop detection methods that can distinguish wild-type and EP402R-deleted ASFV infections. In this study, two recombinant ASFV-p72 and -CD2v proteins were expressed using a prokaryotic system and used to immunize Bactrian camels. Subsequently, eight nanobodies against ASFV-p72 and ten nanobodies against ASFV-CD2v were screened. Following the production of these nanobodies with horse radish peroxidase (HRP) fusion proteins, the ASFV-p72-Nb2-HRP and ASFV-CD2v-Nb22-HRP fusions were selected for the development of two competitive ELISAs (cELISAs) to detect anti-ASFV antibodies. The two cELISAs exhibited high sensitivity, good specificity, repeatability, and stability. The coincidence rate between the two cELISAs and commercial ELISA kits was 98.6% and 97.6%, respectively. Collectively, the two cELISA for detecting antibodies against ASFV demonstrated ease of operation, a low cost, and a simple production process. The two cELISAs could determine whether pigs were infected with wild-type or CD2v-deleted ASFV, and they play an important role in monitoring ASFV infections in pig farms.

Modeling and optimization of sporulation by Bacillus licheniformis BF-002 based on dynamics and recurrent neural networks.

Bacillus licheniformis is widely utilized in disease prevention and environmental remediation. Spore quantity is a critical factor in determining the quality of microbiological agents containing vegetative cells. To improve the understanding of Bacillus licheniformis BF-002 strain culture, a hybrid model integrating traditional dynamic modeling and recurrent neural network was developed. This model enabled the optimization of carbon/nitrogen source feeding rates, pH, temperature and agitation speed using genetic algorithms. Carbon and nitrogen source consumption in the optimal duplicate batches showed no significant difference compared to the control batch. However, the spore quantity in the broth increased by 16.2% and 35.2% in the respective duplicate batches. Overall, the hybrid model outperformed the traditional dynamic model in accurately tracking the cultivation dynamics of Bacillus licheniformis, leading to increased spore production when used for optimizing cultivation conditions.

Enhanced sporulation of B. licheniformis BF-002 through automatic co-feeding of carbon and nitrogen sources.

Bacillus licheniformis formulations are effective for environmental remediation, gut microbiota modulation, and soil improvement. An adequate spore quantity is crucial for the activity of B. licheniformis formulations. This study investigated the synergistic effects of carbon/nitrogen source consumption and concentration on B. licheniformis BF-002 cultivation, with the aim of developing an automatic co-feeding strategy to enhance spore production. Initial glucose (10 g/L) and amino nitrogen (1.5 g/L) concentrations promote cell growth, followed by reduced glucose (2.0 g/L) and amino nitrogen (0.5 g/L) concentrations for sustained spore generation. The spore quantity reached 2.59 × 1010 CFU/mL. An automatic co-feeding strategy was developed and implemented in 5 and 50 L cultivations, resulting in spore quantities of 2.35 × 1010 and 2.86 × 1010 CFU/mL, respectively, improving by 6.81% and 30.00% compared to that with a fixed glucose concentration (10.0 g/L). The culture broth obtained at both the 5 and 50 L scales was spray-dried, resulting in bacterial powder with cell viability rates of 85.94% and 82.68%, respectively. Even after exposure to harsh conditions involving high temperature and humidity, cell viability remained at 72.80% and 69.89%, respectively. Employing the automatic co-feeding strategy increased the transcription levels of the spore formation-related genes spo0A, spoIIGA, bofA, and spoIV by 7.42%, 8.46%, 8.87%, and 9.79%, respectively. The proposed strategy effectively promoted Bacillus growth and spore formation, thereby enhancing the quality of B. licheniformis formulations.

A study on a recommendation algorithm based on spectral clustering and GRU.

With the development of e-commerce, the importance of recommendation algorithms has significantly increased. However, traditional recommendation systems struggle to address issues such as data sparsity and cold start. This article proposes an optimization method for a recommendation system based on spectral clustering (SC) and gated recurrent unit (GRU), named the GRU-KSC algorithm. Firstly, this paper improves the original spectral clustering algorithm by introducing Kmc2, proposing a novel spectral clustering recommendation algorithm (K-means++ SC, KSC) based on the existing SC algorithm. Secondly, building upon the original GRU model, the paper presents a hybrid recommendation algorithm (Hybrid GRU, HGRU) capable of capturing long-term user interests for a more personalized recommendation. Experiments conducted on real datasets demonstrate that our method outperforms existing benchmark methods in terms of accuracy and robustness.

Association between smoking and alcohol drinking and benign adrenal tumors: a Mendelian randomization study.

BACKGROUND: In recent years, the detection rate of adrenal tumors has increased, but it is unclear whether smoking and alcohol drinking are risk factors for benign adrenal tumors. The objective of this study is to employ Mendelian randomization (MR) analysis to explore the causal relationship between smoking, alcohol drinking and susceptibility to benign adrenal tumors. METHODS: We acquired large-scale data from publicly accessible databases on genome-wide association studies (GWAS) pertaining to smoking, alcohol drinking and benign adrenal tumors. A total of 11 sets of instrumental variables (IVs) and 281 associated single nucleotide polymorphic (SNP) loci were identified. The Mendelian randomization analyses were conducted using inverse variance weighting (IVW), MR-Egger regression and weighted median estimation (WME) methods, in addition to sensitivity analyses. RESULTS: There is no causal relationship between smoking status, alcohol drinking status, alcohol intake frequency, alcohol taken with meals, alcohol consumption and benign adrenal tumors, while pack years of smoking and cigarettes per day are risk factors for benign adrenal tumors. The IVW analysis revealed that both the pack years of smoking and cigarettes per day were positively associated with an increased risk of benign adrenal tumors (OR = 2.853, 95%CI = 1.384-5.878, p = 0.004; OR = 1.543, 95%CI = 1.147-2.076, p = 0.004). Two SNPs (rs8042849 in the analysis of pack years of smoking and rs8034191 in the analysis of cigarettes per day) significantly drove the observed causal effects. CONCLUSION: Two-sample Mendelian randomization analysis showed a causal effect between smoking but not alcohol consumption and benign adrenal tumors.

Multi-Omics Analysis Reveals the Role of Changes in Platelet Activation Pathways in the Survival Outcome of Patients with Esophageal Squamous Cell Carcinoma.

Objective: The objective of this study was to integrate metabolomics and transcriptomics data to identify key diagnostic and prognostic markers for esophageal squamous cell carcinoma (ESCC). Plasma samples were collected from 85 ESCC patients at different stages and 50 healthy volunteers for non-targeted metabolomic analysis. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed for non-targeted metabolomic analysis. Subsequently, we integrated the metabolomic data with transcriptomic data from the Gene Expression Omnibus (GEO) and prognosis data from The Cancer Genome Atlas Program (TCGA) to perform pathway analysis. Our focus was on pathways that involve both metabolites and upstream genes, as they often exhibit higher accuracy. Results: Through the integration of metabolomics and transcriptomics, we identified significant alterations in the platelet activation pathway in ESCC. This pathway involves the participation of both metabolites and genes, making it a more accurate reflection of pathological changes associated with the disease. Notably, metabolite arachidonic acid (AA) and chemokine receptor type 2(CXCR2) were significantly downregulated in ESCC, while genes collagen type I alpha 1(COL1A1), collagen type I alpha 2(COL1A2), collagen type III alpha 1(COL3A1), type 3 inositol 1,4,5-trisphosphate receptor (ITPR3), and insulin-like growth factor II mRNA binding protein 3(IGF2BP3) were significantly upregulated, indicating the presence of tumor-induced platelet activation in ESCC. Further analysis of prognosis data revealed that high expression of COL1A1, IGF2BP3, and ITPR3 was associated with a favorable prognosis for ESCC, while high CXCR2 expression was linked to an adverse prognosis. In addition, we combined COL1A1, ITPR3, IGF2BP3, CXCR2, and AA to form a diagnostic biomarker panel. The receiver operating characteristic curve (ROC) demonstrated excellent diagnostic capability (AUC=0.987). Conclusion: Our study underscores the significant role of platelet activation pathways and related genes in the diagnosis and prognosis of ESCC patients. These findings offer promising insights for improving the clinical management of ESCC.

Identification of Breast Cancer Immune-related Prognostic Characteristics in Tumor Microenvironment.

BACKGROUND: Accumulated evidence suggest that tumor microenvironment (TME) plays a crucial role in breast cancer (BRCA) progression and therapeutic effects. OBJECTIVE: This study aimed to characterize immune-related BRCA subtypes in TME, and identify genes with prognostic value. METHODS: RNA sequencing profiles with corresponding clinical data from The Cancer Genome Atlas (TCGA) database of BRCA patients were downloaded to evaluate immune infiltration using the single-sample gene set enrichment (ssGAEA) algorithm. Further, BRCA was clustered according to immune infiltration status by consensus clustering analysis. Using Venn analysis, differentially expressed genes (DEGs) were overlapped to obtain candidate genes. Kaplan-Meier (K-M) analysis was performed to identify prognostic genes, and the results were verified in the GEO and METABRIC datasets. RT-qPCR was conducted to detect the mRNA expression of prognostic genes. RESULTS: In the TCGA database, 3 immune-related BRCA subtypes were identified [cluster1 (C1), cluster2 (C2), and cluster3 (C2)]. The C2 subtype had better overall survival (OS) compared to the C1 subtype. Higher levels of immune markers and checkpoint protein were found in the C2 subtype than in others. By combining DEGs between BRCA and normal tissues, with the C1 and C2 subtypes associated with different OS, 25 BRCA candidate genes were identified. Among these, 8 genes were identified as prognostic genes for BRCA. RT-qPCR showed that the expressions of 2 genes were significantly elevated in BRCA tissues, while that of other genes were decreased. CONCLUSION: Three BRCA subtypes were identified with the immune index, which may help design advanced treatment of BRCA. The data code used for the analysis in this article was available on GitHub (https://github.com/tangzhn/BRCA1.git).

In-Depth Structure and Function Characterization of Heterogeneous Interchain Cysteine-Conjugated Antibody-Drug Conjugates.

Antibody-drug conjugates (ADCs), integrating high specificity of antigen-targeting antibodies and high potency of cell-killing chemical drugs, have become one of the most rapidly expanding therapeutic biologics in oncology. Although ADCs were widely studied from multiple aspects, overall structural elucidation with comprehensive understanding of variants is scarcely reported. Here, for the first time, we present a holistic and in-depth characterization of an interchain cysteine-conjugated ADC, focusing on conjugation and charge heterogeneity, and in vitro biological activities. Conjugation mapping utilized a bottom-up approach, unraveled positional isomer composition, provided insights into the conjugation process, and elucidated how conjugation affects the physicochemical and biological properties of an ADC. Charge profiling combined bottom-up and top-down approaches to interrogate the origin of charge heterogeneity, its impact on function, and best practice for characterization. Specifically, we pioneered the utilization of capillary isoelectric focusing-mass spectrometry to decode not only critical post-translational modifications but also drug load and positional isomer distribution. The study design provides general guidance for in-depth characterization of ADCs, and the analytical findings in turn benefit the discovery and development of future ADCs.

Serum Interleukin-1 Levels Are Associated with Intracranial Aneurysm Instability.

Serum interleukin-1 (IL-1) are possibly indicative of the inflammation in the intracranial aneurysm (IA) wall. This study aimed to investigate whether IL-1 could discriminate the unstable IAs (ruptured intracranial aneurysms (RIAs) and symptomatic unruptured intracranial aneurysms (UIAs)) from stable, asymptomatic UIAs. IA tissues and blood samples from 35 RIA patients and 35 UIA patients were collected between January 2017 and June 2020 as the derivation cohort. Blood samples from 211 patients with UIAs were collected between January 2021 and June 2022 as the validation cohort (including 63 symptomatic UIAs). Blood samples from 35 non-cerebral-edema meningioma patients (non-inflammatory control) and 19 patients with unknown-cause subarachnoid hemorrhage (hemorrhagic control) were also collected. IL-1ß and IL-1.ra (IL-1 receptor antagonist) were measured in serum and IA tissues, and the IL-1 ratio was calculated as log10 (IL-1.ra/IL-1ß). Based on the derivation cohort, multivariate logistic analysis showed that IL-1ß (odds ratio, 1.48, P = 0.001) and IL-1.ra (odds ratio, 0.74, P = 0.005) were associated with RIAs. The IL-1 ratio showed an excellent diagnostic accuracy for RIAs (c-statistic, 0.91). Histological analysis confirmed the significant correlation of IL-1 between serum and aneurysm tissues. IL-1 ratio could discriminate UIAs from non-inflammatory controls (c-statistic, 0.84), and RIAs from hemorrhagic controls (c-statistic, 0.95). Based on the validation cohort, the combination of IL-1 ratio and PHASES score had better diagnostic accuracy for symptomatic UIAs than PHASES score alone (c-statistic, 0.88 vs 0.80, P < 0.001). Serum IL-1 levels correlate with aneurysm tissue IL-1 levels and unstable aneurysm status, and could serve as a potential biomarker for IA instability.

A decision tree model to help treatment decision-making for severe spontaneous intracerebral hemorrhage.

BACKGROUND: Surgical treatment demonstrated a reduction in mortality among patients suffering from severe spontaneous intracerebral hemorrhage (SSICH). However, which SSICH patients could benefit from surgical treatment was unclear. This study aimed to establish and validate a decision tree (DT) model to help determine which SSICH patients could benefit from surgical treatment. MATERIALS AND METHODS: SSICH patients from a prospective, multicenter cohort study were analyzed retrospectively. The primary outcome was the incidence of neurological poor outcome (modified Rankin scale as 4-6) on the 180th day posthemorrhage. Then, surgically-treated SSICH patients were set as the derivation cohort (from a referring hospital) and validation cohort (from multiple hospitals). A DT model to evaluate the risk of 180-day poor outcome was developed within the derivation cohort and validated within the validation cohort. The performance of clinicians in identifying patients with poor outcome before and after the help of the DT model was compared using the area under curve (AUC). RESULTS: One thousand two hundred sixty SSICH patients were included in this study (middle age as 56, and 984 male patients). Surgically-treated patients had a lower incidence of 180-day poor outcome compared to conservatively-treated patients (147/794 vs. 128/466, P <0.001). Based on 794 surgically-treated patients, multivariate logistic analysis revealed the ischemic cerebro-cardiovascular disease history, renal dysfunction, dual antiplatelet therapy, hematoma volume, and Glasgow coma score at admission as poor outcome factors. The DT model, incorporating these above factors, was highly predictive of 180-day poor outcome within the derivation cohort (AUC, 0.94) and validation cohort (AUC, 0.92). Within 794 surgically-treated patients, the DT improved junior clinicians' performance to identify patients at risk for poor outcomes (AUC from 0.81 to 0.89, P <0.001). CONCLUSIONS: This study provided a DT model for predicting the poor outcome of SSICH patients postsurgically, which may serve as a useful tool assisting clinicians in treatment decision-making for SSICH.

Effects of exogenous steroid hormones on growth, body color, and gonadal development in the Opsariichthys bidens.

To investigate the effects of exogenous steroid hormones on growth, body color, and gonadal development in the Opsariichthys bidens (O. bidens), synthetic methyltestosterone (MT) and 17ß-estradiol (E2) were used for 28 days' treatment of 4-month-old O. bidens before the breeding season. Our results suggested that MT had a significant growth-promoting effect (P < 0.05), whereas E2 played an inhibitory role. On the body surface, the females in the MT group showed gray stripes, and the fish in other groups showed no obvious stripes. The males with MT treatment displayed brighter blue-green stripes compared to the CK and E2 groups. The histological analysis showed that the MT significantly promoted testes development in males, blocked oocyte development, and caused massive apoptosis in females, whereas the E2 group promoted ovarian development and inhibited testes development. Based on qRT-PCR analysis, in females, the expression of igf-1, dmrt1, and cyp19a1a genes revealed that E2 treatment resulted in down-regulation of igf-1 expression and up-regulation of cyp19a1a expression. In males, igf-1 and dmrt1 were significantly up-regulated after MT treatment, and E2 treatment led to down-regulation of igf-1. Therefore, this study demonstrates that MT and E2 play an important role in reversing the morphological sex characteristics of females and males.

Robust All-Solid-State Lithium Metal Batteries Enabled by a Composite Lithium Anode with Improved Bulk Li Diffusion Kinetics Properties.

All-solid-state batteries (ASSBs) with a Li metal anode are expected to be one of the most promising energy storage systems to achieve high energy density. However, the interfacial instability between the Li metal anode and solid-state electrolyte (SSE) limits the rate capability and cycling stability of ASSBs. The main issue is the formation of voids at the Li/SSE interface during Li stripping due to the slow diffusion of Li within the bulk Li metal, then increasing internal cell resistance and inducing the formation of lithium dendrites. To address these issues, a composite Li anode (LAO) composed by Li-Ag alloy and Li2O is constructed by mixing the stoichiometric metal Li and Ag2O directly. LAO anode is capable of improving bulk Li diffusion kinetics and inhibiting the formation of interfacial voids effectively, achieving a high critical current density over 1.5 mA cm-2 and long stable cycling over 1000 h at 1 mA cm-2. The ASSBs, employing LAO as the anode, Li6PS5Cl as the SSE, and LiCoO2 as the cathode, exhibit superior rate capability and stable cycling over 4000 cycles at 5 C. Moreover, ASSBs can operate stably with a high LiCoO2 loading of 17.8 mg cm-2 for more than 100 cycles at 0.2 C.

Resumption of production operations during the silent disposal period of African swine fever outbreaks: a case study.

BACKGROUND: Batch production, a widely implemented production model in large-scale pig farms, was characterized by its long-term duration, cost-effectiveness, and efficiency. Nevertheless, the recent occurrence of African swine fever (ASF) outbreaks in China has necessitated the implementation of discreet mating operations within this model, leading to disruptions in production cycles and substantial indirect losses. CASE PRESENTATION: This study implemented a novel operational procedure, which involved the division of risk areas for zone management and allowed mating operations, in 12 farms experiencing ASF outbreaks. Another 12 farms were used as a control group, employing the old procedure. Subsequently, the prognoses of both the old and new procedures were calculated and analyzed. The findings indicate that the new method resulted in an enhanced retention rate and reduced non-productive days (NPD), without impacting the positive detection rate and disposal time. Consequently, this approach significantly mitigated economic losses (p < 0.05). CONCLUSION: The efficacy of the novel procedure in mitigating the indirect economic losses stemming from ASF outbreaks, through the reduction of NPD while maintaining retention rates and disposition days, has been substantiated. This methodology has demonstrated feasibility in extensive pig farming operations and exhibits promise for broader application.

A deep-learning system to help make the surgical planning of coil embolization for unruptured intracranial aneurysms.

BACKGROUND: Coil embolization is a common method for treating unruptured intracranial aneurysms (UIAs). To effectively perform coil embolization for UIAs, clinicians must undergo extensive training with the assistance of senior physicians over an extended period. This study aimed to establish a deep-learning system for measuring the morphological features of UIAs and help the surgical planning of coil embolization for UIAs. METHODS: Preoperative computational tomography angiography (CTA) data and surgical data from UIA patients receiving coil embolization in our medical institution were retrospectively reviewed. A convolutional neural network (CNN) model was trained on the preoperative CTA data, and the morphological features of UIAs were measured automatically using this CNN model. The intraclass correlation coefficient (ICC) was utilized to examine the similarity between the morphologies measured by the CNN model and those determined by experienced clinicians. A deep neural network model to determine the diameter of first coil was further established based on the CNN model within the derivation set (75% of all patients) using neural factorization machines (NFM) model and was validated using a validation set (25% of all patients). The general match ratio (the difference was within ± 1 mm) between the predicted diameter of first coil by model and that used in practical scenario was calculated. RESULTS: One-hundred fifty-three UIA patients were enrolled in this study. The CNN model could diagnose UIAs with an accuracy of 0.97. The performance of this CNN model in measuring the morphological features of UIAs (i.e., size, height, neck diameter, dome diameter, and volume) was comparable to the accuracy of senior clinicians (all ICC > 0.85). The diameter of first coil predicted by the model established based on CNN model and the diameter of first coil used actually exhibited a high general match ratio (0.90) within the derivation set. Moreover, the model performed well in recommending the diameter of first coil within the validation set (general match ratio as 0.91). CONCLUSION: This study presents a deep-learning system which can help to improve surgical planning of coil embolization for UIAs.

Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor with an unsatisfactory prognosis. This study aims to identify the expression patterns of disulfidptosis-related genes (DRGs), develop a prognostic model, and predict immunological profiles. METHODS: First, we identified differentially expressed DRGs in TCGA-KIRC cohort and analyzed their mutational profiles, methylation levels, and interaction networks. Subsequently, we identified disulfidptosis-associated molecular subtypes and investigated their prognostic and immunological characteristics. Simultaneously, a disulfidptosis-related prognostic signature (DRPS) was developed using a two-stage stacking framework consisting of 5 machine learning models. The effect of DRPS on immune cell infiltration levels was explored using seven different algorithms, and the status and function of T cells for distinct risk-score groups were evaluated based on T cell exhaustion and dysfunction scores. Additionally, the study also examined differences in clinical characteristics and therapy efficacy between high- and low-risk groups. RESULTS: We found two disulfidptosis-associated clusters, one of which had a poor prognosis and was linked to high immune cell infiltration but impaired T cell function. DRPS showed excellent predictive performance in all four cohorts and could accurately identified disulfidptosis-related molecular subtypes. The DRPS-based risk score was positively associated with poor prognosis, malignant pathological features, high immune cell infiltration levels, and T cell exhaustion or dysfunction, and better respond to immunotherapy and targeted therapy. Additionally, we have identified a close association between ISG20 and disulfidptosis as well as tumor immunity. CONCLUSION: Our study identified distinct disulfidptosis-related subtypes in ccRCC patients, and constructed the highly accurate and robust DRPS based on an ensemble learning framework, which has critical reference value in clinical decision-making and individualized treatment. And this work also revealed ISG20 exhibits promising potential as a therapeutic target for ccRCC.

Model based on single-nucleotide polymorphism to discriminate aspirin resistance patients.

BACKGROUND: Aspirin is widely used for preventing ischaemic events. About 20%-40% of patients have aspirin resistance (ASR), which prevents them from benefiting from aspirin medication. This study aimed to develop and validate a model based on single-nucleotide polymorphism (SNP) to distinguish ASR patients. METHODS: We included patients with spontaneous intracerebral haemorrhage and continuing antiplatelet therapy from a multicentre, prospective cohort study as the derivation cohort. Thromboelastography (inhibition of arachidonic acid channel<50%) was used to identify ASR. Genotyping was performed to identify the ASR-related SNP. Based on the result of the logistic analysis, the aspirin resistance in the Chinese population score (ASR-CN score) was established, and its accuracy was evaluated using the area under the curve (AUC). Patients receiving dual antiplatelet therapy for unruptured intracranial aneurysm embolism were prospectively included in the validation cohort. After embolism, 30-day ischaemic events, including ischaemic stroke, new or more frequent transient ischaemic attack, stent thrombosis and cerebrovascular death, were recorded. RESULTS: The derivation cohort included 212 patients (155 male patients and the median age as 59). 87 (41.0%) individuals were identified with ASR. The multivariate logistic analysis demonstrated six SNPs of GP1BA, TBXA2R, PTGS2 and NOS3 as risk factors related to ASR. The ASR-CN score integrating these SNPs performed well to discriminate ASR patients from non-ASR patients (AUC as 0.77). Based on the validation cohort of 372 patients receiving antiplatelet therapy after embolism (including 130 ASR patients), the ASR-CN score continued to distinguish ASR patients with good accuracy (AUC as 0.80). Patients with high a ASR-CN score were more likely to suffer from 30-day ischaemic events after embolism (OR, 1.28; 95% CI, 1.10 to 1.50; p=0.002). CONCLUSION: GP1BA, TBXA2R, PTGS2 and NOS3 were SNPs related to ASR. The ASR-CN score is an effective tool to discriminate ASR patients, which may guide antiplatelet therapy. CLINICAL TRIAL REGISTRATION: Surgical Treatments of Antiplatelet Intracerebral Hemorrhage cohort (unique identifier: ChiCTR1900024406, http://www.chictr.org.cn/edit.aspx?pid=40640&htm=4).

Methylprednisolone is related to lower incidence of postoperative bleeding after flow diverter treatment for unruptured intracranial aneurysm.

Background and objectives: Regarding the anti-inflammatory effect, methylprednisolone is a candidate to prevent patients with unruptured intracranial aneurysms (UIAs) from postoperative bleeding (PB) after flow diverter (FD) treatment. This study aimed to investigate whether methylprednisolone is related to a lower incidence of PB after FD treatment for UIAs. Methods: This study retrospectively reviewed UIA patients receiving FD treatment between October 2015 and July 2021. All patients were observed until 72 h after FD treatment. The patients receiving methylprednisolone (80 mg, bid, for at least 24 h) were considered as standard methylprednisolone treatment (SMT) users, otherwise as non-SMT users. The primary endpoint indicated the occurrence of PB, including subarachnoid hemorrhage, intracerebral hemorrhage, and ventricular bleeding, within 72 h after FD treatment. This study compared the incidence of PB between SMT users and non-SMT users and investigated the protective effect of SMT on PB after FD treatment using the Cox regression model. Finally, after controlling the potential factors related to PB, we performed subgroup analysis to further confirm the protective effect of SMT on PB. Results: This study finally included 262 UIA patients receiving FD treatment. PB occurred in 11 patients (4.2%), and 116 patients (44.3%) received SMT postoperatively. The median time from the end of surgery to PB was 12.3 h (range: 0.5-48.0 h). SMT users had a lower incidence of PB comparing with non-SMT users (1/116, 0.9% vs. 10/146, 6.8%, respectively; p = 0.017). The multivariate Cox analysis demonstrated that SMT users (HR, 0.12 [95%CI, 0.02-0.94], p = 0.044) had a lower risk of PB postoperatively. After controlling the potential factors related to PB (i.e., gender, irregular shape, surgical methods [FD and FD + coil] and UIA sizes), the patients receiving SMT still had a lower cumulative incidence of PB, comparing with patients receiving non-SMT (all p < 0.05). Conclusion: SMT was correlated with the lower incidence of PB for patients receiving FD treatment and may be a potential method to prevent PB after the FD treatment.

The markers and risk stratification model of intracranial aneurysm instability in a large Chinese cohort.

Intracranial aneurysm is the leading cause of nontraumatic subarachnoid hemorrhage. Evaluating the unstable (rupture and growth) risk of aneurysms is helpful to guild decision-making for unruptured intracranial aneurysms (UIA). This study aimed to develop a model for risk stratification of UIA instability. The UIA patients from two prospective, longitudinal multicenter Chinese cohorts recruited from January 2017 to January 2022 were set as the derivation cohort and validation cohort. The primary endpoint was UIA instability, comprising aneurysm rupture, growth, or morphology change, during a 2-year follow-up. Intracranial aneurysm samples and corresponding serums from 20 patients were also collected. Metabolomics and cytokine profiling analysis were performed on the derivation cohort (758 single-UIA patients harboring 676 stable UIAs and 82 unstable UIAs). Oleic acid (OA), arachidonic acid (AA), interleukin 1ß (IL-1ß), and tumor necrosis factor-α (TNF-α) were significantly dysregulated between stable and unstable UIAs. OA and AA exhibited the same dysregulated trends in serums and aneurysm tissues. The feature selection process demonstrated size ratio, irregular shape, OA, AA, IL-1ß, and TNF-α as features of UIA instability. A machine-learning stratification model (instability classifier) was constructed based on radiological features and biomarkers, with high accuracy to evaluate UIA instability risk (area under curve (AUC), 0.94). Within the validation cohort (492 single-UIA patients harboring 414 stable UIAs and 78 unstable UIAs), the instability classifier performed well to evaluate the risk of UIA instability (AUC, 0.89). Supplementation of OA and pharmacological inhibition of IL-1ß and TNF-α could prevent intracranial aneurysms from rupturing in rat models. This study revealed the markers of UIA instability and provided a risk stratification model, which may guide treatment decision-making for UIAs.

Serum interleukin-1 is a new biomarker to predict the risk of rebleeding of ruptured intracranial aneurysm after admission.

Interleukin-1 (IL-1) could induce inflammation of the aneurysm wall, which might be related to intracranial aneurysm rupture. The aim of this study was to investigate whether IL-1 could serve as a biomarker to predict the risk of rebleeding after admission. Data between January 2018 and September 2020 were collected from patients with ruptured intracranial aneurysms (RIAs) and were retrospectively reviewed. The serum IL-1ß and IL-1ra levels were detected using a panel, and IL-1 ratio was calculated as the log10 (IL-1ra/IL-1ß). The predictive accuracy of IL-1 compared with previous clinical morphology (CM) model and other risk factors were evaluated by the c-statistic. Five hundred thirty-eight patients were finally included in the study, with 86 rebleeding RIAs. The multivariate Cox analysis confirmed aspect ratio (AR) > 1.6 (hazard ratio (HR), 4.89 [95%CI, 2.76-8.64], P < 0.001), size ratio (SR) > 3.0 (HR, 2.40 [95%CI, 1.34-4.29], P = 0.003), higher serum IL-1ß (HR, 1.88 [95%CI, 1.27-2.78], P = 0.002), and lower serum IL-1ra (HR, 0.67 [95%CI, 0.56-0.79], P < 0.001) as the independent risk factors for rebleeding after admission. According to the c-statistics, the IL-1 ratio had the highest predictive accuracy (0.82), followed by IL-1ra and IL-1ß (0.80), AR > 1.6 (0.79), IL-1ra (0.78), IL-1ß (0.74), and SR > 3.0 (0.56), respectively. Subgroup analysis based on AR and SR presented similar results. The model combining IL-1 ratio and CM model showed higher predictive accuracy for the rebleeding after admission (c-statistic, 0.90). Serum IL-1, especially IL-1 ratio, could serve as a biomarker to predict the risk of rebleeding after admission.

Preoperative α-blockade versus no blockade for pheochromocytoma-paraganglioma patients undergoing surgery: a systematic review and updated meta-analysis.

BACKGROUND: Surgical resection of pheochromocytomas and paragangliomas (PPGLs) is associated with a significant risk of intraoperative hemodynamic instability and cardiovascular complications. α-blockade remains the routine preoperative medical preparation despite controversies over the lack of evidence. We presented an updated meta-analysis to ulteriorly evaluate the potential efficacy of preoperative α-blockade versus no blockade for PPGL patients undergoing surgery. MATERIALS AND METHODS: Randomized and nonrandomized comparative studies assessing preoperative α-blockade for PPGL surgery in adults were identified through a systematic literature search via MEDLINE, Embase, Web of Science, and CENTRAL up to November 2022. Outcome data of intraoperative hemodynamic parameters and major postoperative events were extracted. Mean difference and risk ratio were synthesized as appropriate for each outcome to determine the cumulative effect size. RESULTS: Fifteen nonrandomized studies involving 3542 patients were finally eligible. Intraoperatively, none of the analyzed hemodynamic parameters differed between patients with or without α-blockade: maximum and minimum systolic blood pressure, hypertensive and hypotensive hemodynamic instability episodes, and peak heart rate, subgroup analysis of normotensive PPGL patients yielded similar results with the overall effects. Postoperatively, α-blockade was associated with prolonged hypotension and vasopressor usage (risk ratio: 4.21, 95% CI: 1.17-15.18, P =0.03). ICU admission, length of stay, overall cardiovascular morbidity, and mortality were similar between the two groups. CONCLUSIONS: Preoperative α-blockade ensured neither more stable intraoperative hemodynamics nor better perioperative outcome over no blockade for PPGL surgery. However, large-volume randomized controlled trials are still warranted to ascertain these findings.