Legionella effectors SidC/SdcA ubiquitinate multiple small GTPases and SNARE proteins to promote phagosomal maturation.

Protein ubiquitination is one of the most important posttranslational modifications (PTMs) in eukaryotes and is involved in the regulation of almost all cellular signaling pathways. The intracellular bacterial pathogen Legionella pneumophila translocates at least 26 effectors to hijack host ubiquitination signaling via distinct mechanisms. Among these effectors, SidC/SdcA are novel E3 ubiquitin ligases with the adoption of a Cys-His-Asp catalytic triad. SidC/SdcA are critical for the recruitment of endoplasmic reticulum (ER)-derived vesicles to the Legionella-containing vacuole (LCV). However, the ubiquitination targets of SidC/SdcA are largely unknown, which restricts our understanding of the mechanisms used by these effectors to hijack the vesicle trafficking pathway. Here, we demonstrated that multiple Rab small GTPases and target soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNARE) proteins are bona fide ubiquitination substrates of SidC/SdcA. SidC/SdcA-mediated ubiquitination of syntaxin 3 and syntaxin 4 promotes their unconventional pairing with the vesicle-SNARE protein Sec22b, thereby contributing to the membrane fusion of ER-derived vesicles with the phagosome. In addition, our data reveal that ubiquitination of Rab7 by SidC/SdcA is critical for its association with the LCV membrane. Rab7 ubiquitination could impair its binding with the downstream effector Rab-interacting lysosomal protein (RILP), which partially explains why LCVs avoid fusion with lysosomes despite the acquisition of Rab7. Taken together, our study reveals the biological mechanisms employed by SidC/SdcA to promote the maturation of the LCVs.

Trans-electrode pressure of gas-diffusion electrodes significantly influencing the electrochemical hydrogen peroxide production.

Hydrogen peroxide (H2O2) synthesis by electrochemical two-electron oxygen reduction has garnered increasing interest as a wide range of potential applications. Gas diffusion electrodes (GDEs) can effectively promote the H2O2 production efficiency by overcoming the oxygen mass transfer limitations but strongly influenced by the electrowetting process along the long-term operation. In this study, the effect of trans-electrode pressure (TEP) of GDE cathode on the electrowetting process was further elucidated. We controlled the TEP values of four types of GDEs: two Ni-based GDEs and two carbon cloth GDEs prepared by hot-pressing or brushing carbon black. SBA-15 was further used to regulate the microstructure of one Ni-based GDE. It was found that an optimal range of TEP occurred for all tested GDEs in terms of the max. concentration, the yield efficiency, the energy consumption, and the stability because TEP may change the triple-phase interface and influence the anti-electrowetting effect. The porosity of hot-pressed Ni GDE can maintain the TEP window and thus enhance the production of H2O2, likely via creating oxygen-containing functional groups and a bimodal pore structure on the electrode, revealed with several characterization techniques including SEM, CA, XPS, Raman spectra, CV and EIS. The porous Ni GDE presented an efficient and stable production of H2O2 for 10 cycles: yielding H2O2 at 4393.2-4602.2 mmol m-2 h-1 with current efficiencies of 94.2-98.7%. The best accumulated H2O2 concentration can reach up to 3.58 ωt% H2O2 at 10 h. The results provide an important reference for the industrial scaleup of electro-production of H2O2 with GDEs.

ITPRIPL1 binds CD3ε to impede T cell activation and enable tumor immune evasion.

Cancer immunotherapy has transformed treatment possibilities, but its effectiveness differs significantly among patients, indicating the presence of alternative pathways for immune evasion. Here, we show that ITPRIPL1 functions as an inhibitory ligand of CD3ε, and its expression inhibits T cells in the tumor microenvironment. The binding of ITPRIPL1 extracellular domain to CD3ε on T cells significantly decreased calcium influx and ZAP70 phosphorylation, impeding initial T cell activation. Treatment with a neutralizing antibody against ITPRIPL1 restrained tumor growth and promoted T cell infiltration in mouse models across various solid tumor types. The antibody targeting canine ITPRIPL1 exhibited notable therapeutic efficacy against naturally occurring tumors in pet clinics. These findings highlight the role of ITPRIPL1 (or CD3L1, CD3ε ligand 1) in impeding T cell activation during the critical "signal one" phase. This discovery positions ITPRIPL1 as a promising therapeutic target against multiple tumor types.

Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison.

BACKGROUND: Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. METHODS: The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. RESULTS: We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. CONCLUSIONS: NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

Deep learning-assisted diagnosis of large vessel occlusion in acute ischemic stroke based on four-dimensional computed tomography angiography.

Purpose: To develop deep learning models based on four-dimensional computed tomography angiography (4D-CTA) images for automatic detection of large vessel occlusion (LVO) in the anterior circulation that cause acute ischemic stroke. Methods: This retrospective study included 104 LVO patients and 105 non-LVO patients for deep learning models development. Another 30 LVO patients and 31 non-LVO patients formed the time-independent validation set. Four phases of 4D-CTA (arterial phase P1, arterial-venous phase P2, venous phase P3 and late venous phase P4) were arranged and combined and two input methods was used: combined input and superimposed input. Totally 26 models were constructed using a modified HRNet network. Assessment metrics included the areas under the curve (AUC), accuracy, sensitivity, specificity and F1 score. Kappa analysis was performed to assess inter-rater agreement between the best model and radiologists of different seniority. Results: The P1 + P2 model (combined input) had the best diagnostic performance. In the internal validation set, the AUC was 0.975 (95%CI: 0.878-0.999), accuracy was 0.911, sensitivity was 0.889, specificity was 0.944, and the F1 score was 0.909. In the time-independent validation set, the model demonstrated consistently high performance with an AUC of 0.942 (95%CI: 0.851-0.986), accuracy of 0.902, sensitivity of 0.867, specificity of 0.935, and an F1 score of 0.901. The best model showed strong consistency with the diagnostic efficacy of three radiologists of different seniority (k = 0.84, 0.80, 0.70, respectively). Conclusion: The deep learning model, using combined arterial and arterial-venous phase, was highly effective in detecting LVO, alerting radiologists to speed up the diagnosis.

Set stabilization of logical control networks: A minimum node control approach.

In network systems, control using minimum nodes or pinning control can be effectively used for stabilization problems to cut down the cost of control. In this paper, we investigate the set stabilization problem of logical control networks. In particular, we study the set stabilization problem of probabilistic Boolean networks (PBNs) and probabilistic Boolean control networks (PBCNs) via controlling minimal nodes. Firstly, an algorithm is given to search for the minimum index set of pinning nodes. Then, based on the analysis of its high computational complexity, we present optimized algorithms with lower computational complexity to ascertain the network control using minimum node sets. Moreover, some sufficient and necessary conditions are proposed to ensure the feasibility and effectiveness of the proposed algorithms. Furthermore, a theorem is presented for PBCNs to devise all state-feedback controllers corresponding to the set of pinning nodes. Finally, two models of gene regulatory networks are considered to show the efficacy of obtained results.

Discovery of galectin-8 as an LILRB4 ligand driving M-MDSCs defines a class of antibodies to fight solid tumors.

LILRB4 is an immunosuppressive receptor, and its targeting drugs are undergoing multiple preclinical and clinical trials. Currently, the absence of a functional LILRB4 ligand in solid tumors not only limits the strategy of early antibody screening but also leads to the lack of companion diagnostic (CDx) criteria, which is critical to the objective response rate in early-stage clinical trials. Here, we show that galectin-8 (Gal-8) is a high-affinity functional ligand of LILRB4, and its ligation induces M-MDSC by activating STAT3 and inhibiting NF-κB. Significantly, Gal-8, but not APOE, can induce MDSC, and both ligands bind LILRB4 noncompetitively. Gal-8 expression promotes in vivo tumor growth in mice, and the knockout of LILRB4 attenuates tumor growth in this context. Antibodies capable of functionally blocking Gal-8 are able to suppress tumor growth in vivo. These results identify Gal-8 as an MDSC-driving ligand of LILRB4, and they redefine a class of antibodies for solid tumors.

Ultrasound had high accuracy in measuring hip joint capsule thickness.

BACKGROUND: The hip joint capsule is an essential component of hip joint function and stability, and its thickness is closely associated with certain medical conditions, surgical outcomes, and rehabilitation treatments. Currently, in clinical practice, hip joint capsule thickness is predominantly measured using magnetic resonance imaging (MRI), with limited utilization of ultrasound examinations for this purpose. METHODS: We retrospectively evaluated patients who visited our Sports Medicine Department between February 2017 and March 2023 and underwent both hip joint MRI and ultrasound imaging on the same side. All patients had undergone preoperative hip joint MRI and ultrasound examinations, with the time gap between the two examinations not exceeding three months. Measurements of hip joint capsule thickness were taken on both MRI and ultrasound images for the same patients to analyze their consistency. Additionally, we measured the alpha angle, lateral center-edge angle, acetabular anteversion angle, and femoral anteversion angle of the patients' hip joints and analyzed their correlation with hip joint capsule thickness measure by ultrasound. RESULTS: A total of 307 patients were included in this study, with hip joint capsule thickness measured by MRI and ultrasound being 5.0 ± 1.2 mm and 5.0 ± 1.5 mm, respectively. The Bland-Altman analysis demonstrates good agreement or consistency. The paired t-test resulted in a p-value of 0.708, indicating no significant statistical difference between the two methods. The correlation analysis between acetabular anteversion angle and ultrasound-measured capsule thickness yielded a p-value of 0.043, indicating acetabular anteversion angle and capsular thickness may have negative correlation. CONCLUSIONS: The measurements of joint capsule thickness obtained through ultrasound and MRI showed good consistency, suggesting that ultrasound can be used in clinical practice as a replacement for MRI in measuring hip joint capsule thickness. There was a significant correlation between acetabular anteversion angle and hip joint capsule thickness, indicating potential for further research in this area.

Thermosensitive polymer-assisted extraction and purification of fungal laccase from citrus pulp wash effluent.

BACKGROUND: This study explores the use of liquid-liquid extraction with thermosensitive polymers for producing laccase (Lac) from Pleurotus sajor-caju. This process leverages liquid waste from the citrus industry, specifically pulp wash. The research delves into extractive fermentation and thermoseparation, both processes being facilitated by a polymer exhibiting a lower critical solution temperature transition. RESULTS: Key factors considered include the choice of polymer, its concentration, pH, separation temperature, and the behavior of the polymer-rich phase post-extractive fermentation concerning the lower critical solution temperature. Notably, under conditions of 45% by weight of Pluronic L-61 and pH 5.0 at 25 °C, the Lac resulted in an enhancement in the purification factor of 28.4-fold, compared with the Lac obtained directly from the fermentation process on the eighth day. There was an 83.6% recovery of the Lac enzyme in the bottom phase of the system. Additionally, the unique properties of Pluronic L-61, which can induce phase separation and also allow for thermoseparation, led to a secondary fraction (aqueous solution) of Lac with purification factor of 2.1 ± 0.1-fold (at 32 ± 0.9 °C and 30 ± 0.3 min without stirring) from the polymeric phase (top phase). Fourier-transform infrared analysis validated the separation data, particularly highlighting the α-helix content in the amide I region (1600-1700 cm-1 ). CONCLUSION: In summary, the insights from this study pave the way for broader industrial applications of these techniques, underscoring benefits like streamlined process integration, heightened selectivity, and superior separation efficacy. © 2023 Society of Chemical Industry.

Aromatic Ring-Fused Dipyrrins: Programmed [4 + 2]-Cycloaddition Pathway with Regio-selectivity upon Alkylamino-Substitution.

Sulfolenodipyrrins are employed as building blocks to concisely and efficiently construct aromatic rings (e.g., naphthoquinone, anthraquinone, fullerenes, and phthalimide) from fused dipyrrins by programmed [4 + 2]-cycloaddition reactions. Notably, alkylamino-substitution at the α-position not only enhances the reactivity of sulfolenodipyrrins but also results in the regio-selectivity of the cycloaddition reactions. Theoretical calculations in terms of frontier orbitals of dienes, energy of dienes, steric hindrance, and aromaticity have been conducted to understand the reason in depth. Additionally, the fusion of aromatic groups enables bathochromic absorption with up to ∼130 nm for the monoadducts and to ∼200 nm for the bis-adducts. The phthalimide annulation dipyrrin displays red emission, while the other mono- or bis-adducts do not, owing to the presence of typical acceptors such as quinone analogs or fullerene.

A Novel 3D-Printed Negative-Stiffness Lattice Structure with Internal Resonance Characteristics and Tunable Bandgap Properties.

The bandgap tuning potential offered by negative-stiffness lattice structures, characterized by their unique mechanical properties, represents a promising and burgeoning field. The potential of large deformations in lattice structures to transition between stable configurations is explored in this study. This transformation offers a novel method for modifying the frequency range of elastic wave attenuation, simultaneously absorbing energy and effectively generating diverse bandgap ranges. In this paper, an enhanced lattice structure is introduced, building upon the foundation of the normal negative-stiffness lattice structures. The research examined the behavior of the suggested negative-stiffness lattice structures when subjected to uniaxial compression. This included analyzing the dispersion spectra and bandgaps across different states of deformation. It also delved into the effects of geometric parameter changes on bandgap properties. Furthermore, the findings highlight that the normal negative-stiffness lattice structure demonstrates restricted capabilities in attenuating vibrations. In contrast, notable performance improvements are displayed by the improved negative-stiffness lattice structure, featuring distinct energy band structures and variable bandgap ranges in response to differing deformation states. This highlights the feasibility of bandgap tuning through the deformation of negatively stiffened structures. Finally, the overall metamaterial structure is simulated using a unit cell finite element dynamic model, and its vibration transmission properties and frequency response patterns are analyzed. A fresh perspective on the research and design of negative-stiffness lattice structures, particularly focusing on their bandgap tuning capabilities, is offered in this study.

Imaging features of cardioembolic stroke on 4-dimensional computed tomography angiography.

Background: Identifying cardioembolic stroke is important for the decision-making of endovascular treatment and anticoagulation therapy. We aimed to explore the features of cardioembolic stroke on 4-dimensional (4D) computed tomography angiography (4D-CTA) and assess whether these features can assist in classifying stroke etiology. Methods: In this retrospective study, we analyzed the images of 294 patients with acute ischemic stroke (AIS) from July 2020 to February 2022 at the First Affiliated Hospital of Chongqing Medical University, which had been consecutively collected. The data of 110 patients with occlusion of the M1/M2 segment of the middle cerebral artery (MCA) with/without intracranial internal carotid artery (ICA) occlusion were analyzed to calculate the clot burden score (CBS) and collateral score (CS), and the data of 88 patients with a clear origin and distal part were analyzed to measure clot length. Maximum intensity projection (MIP) and time MIP (tMIP) post-processing were used to assess the clot features. The Mann-Whitney U test was used to compare the clot characteristics between the 2 groups. Binary logistic regression was performed to assess the association between the image characteristics and cardioembolic stroke. Moreover, the receiver operating characteristic (ROC) curve was used to test the diagnostic efficacy of MIP/tMIP clot features in classifying cardioembolic stroke. Results: Age, high-risk factors for cerebrovascular disease, high/medium-risk sources of cardioembolic stroke, clot length, CBS, and CS were significantly different between the cardioembolic stroke group and non-cardioembolic stroke group (P<0.05). In the cardioembolic stroke group, the median MIP and tMIP clot length was 12 mm [interquartile range (IQR), 8.3-17.4 mm] and 9.3 mm (IQR, 6.8-14.3 mm), respectively. In the non-cardioembolic stroke group, the median MIP and tMIP clot length was 6.5 mm (IQR, 4.7-11.5 mm) and 5.8 mm (IQR, 3.9-10.6 mm), respectively. Binary logistic regression showed that cardioembolic stroke was significantly associated with MIP-clot length [odds ratio (OR), 1.15; 95% confidence interval (CI): 1.02-1.29; P<0.05], tMIP-clot length (OR, 1.18; 95% CI: 1.02-1.36; P<0.05), and tMIP-CBS (OR, 3.96; 95% CI: 1.08-14.58; P<0.05). The area under the ROC curve (AUC) values of MIP clot length for identifying cardioembolic stroke were 0.75 (95% CI: 0.65-0.84, P<0.05), with a cut-off value of >7.4 mm [sensitivity: 84.62% (95% CI: 69.50-94.10%); specificity: 59.18% (95% CI: 44.20-73.00%)]. The AUC value of tMIP clot length was 0.72 (95% CI: 0.61-0.81, P<0.05), with a cut-off value of >5.4 mm [sensitivity: 92.31% (95% CI: 79.10-98.40%); specificity: 48.98% (95% CI: 34.40-63.70%)]. Conclusions: Clot length and CBS were overestimated on MIP images. Among the clot characteristics, clot length could identify cardioembolic stroke.

Targeting collagen in tumor extracellular matrix as a novel targeted strategy in cancer immunotherapy.

Collagen, the most abundant protein in mammal, is widely expressed in tissues and organs, as well as tumor extracellular matrix. Tumor collagen mainly accumulates in tumor stroma or beneath tumor blood vessel endothelium, and is exposed due to the fragmentary structure of tumor blood vessels. Through the blood vessels with enhanced permeability and retention (EPR) effect, collagen-binding macromolecules could easily bind to tumor collagen and accumulate within tumor, supporting tumor collagen to be a potential tumor-specific target. Recently, numerous studies have verified that targeting collagen within tumor extracellular matrix (TEM) would enhance the accumulation and retention of immunotherapy drugs at tumor, significantly improving their anti-tumor efficacy, as well as avoiding severe adverse effects. In this review, we would summarize the known collagen-binding domains (CBD) or proteins (CBP), their mechanism and application in tumor-targeting immunotherapy, and look forward to future development.

A collagen-binding SIRPαFc fusion protein for targeted cancer immunotherapy.

Collagen is abundant but exposed in tumor due to the abnormal tumor blood vessels, thus is considered as a tumor-specific target. The A3 domain of von Willebrand factor (vWF A3) is a kind of collagen-binding domain (CBD) which could bind collagen specifically. Previously we reported a chemosynthetic CBD-SIRPαFc conjugate, which could block CD47 and derived tumor-targeting ability by CBD. CBD-SIRPαFc conjugate represented improved anti-tumor efficacy with increased MHC II+ M1 macrophages, but the uncertain coupling ratio remained a problem. Herein, we produced a vWF A3-SIRPαFc fusion protein through eukaryotic expression system. It was examined at both molecular and cellular levels with its collagen affinity, uninfluenced original affinity to targets and phagocytosis-promoting function compared to unmodified SIRPαFc. Living imaging showed that vWF A3-SIRPαFc fusion protein derived the improved accumulation and retention in tumor than SIRPαFc. In the MC38 allograft model, vWF A3-SIRPαFc demonstrated a superior tumor-suppressing effect, characterized by increased MHC II+ M1 macrophages and T cells (particularly CD4+ T cells). These results revealed that vWF A3-SIRPαFc fusion protein derived tumor-targeting ability, leading to improved anti-tumor immunotherapeutic efficacy compared to SIRPαFc. Altogether, vWF A3 improved the anti-tumor efficacy and immune-activating function of SIRPαFc, supporting targeting tumor collagen as a possible targeted strategy.

Agricultural waste biochar after potassium hydroxide activation: Its adsorbent evaluation and potential mechanism.

The agricultural waste (Goji branch) was pyrolyzed into biochars with one-step potassium hydroxide (KOH) activation under different processing conditions. The biochars were first characterized in structural features and functional groups and then evaluated for adsorptive performance with methylene blue as a model pollutant. Different adsorption models were applied to fit the adsorption process and reveal the possible mechanisms. The adsorption capacity was found to strongly correlate (R2 = 0.9642) with the surface area of the biochars, among which biochar K50%W29%C-700 (pyrolysis at 700 °C in the presence of 50 % KOH and 29 % water) possessed the largest surface area (1378 m2/g) and exhibited the highest adsorption capacity (769 mg/g) compared to its homologous products. Biochar K50%W29%C-700 also showed excellent recyclability and potent adsorption capacity toward other common organic pollutants. The results suggest that traces of water in agricultural wastes could significantly intensify the KOH-involved activation efficiency of producing porous biochar.

Automatic Evaluating of Multi-Phase Cranial CTA Collateral Circulation Based on Feature Fusion Attention Network Model.

Stroke is one of the main causes of disability and death, and it can be divided into hemorrhagic stroke and ischemic stroke. Ischemic stroke is more common, and about 8 out of 10 stroke patients suffer from ischemic stroke. In clinical practice, doctors diagnose stroke by using computed tomography angiography (CTA) image to accurately evaluate the collateral circulation in stroke patients. This imaging information is of great significance in assisting doctors to determine the patient's treatment plan and prognosis. Currently, great progress has been made in the field of computer-aided diagnosis technology in medicine by using artificial intelligence. However, in related research based on deep learning algorithms, researchers usually only use single-phase data for training, lacking the temporal dimension information of multi-phase image data. This makes it difficult for the model to learn more comprehensive and effective collateral circulation feature representation, thereby limiting its performance. Therefore, combining data for training is expected to improve the accuracy and reliability of collateral circulation evaluation. In this study, we propose an effective hybrid mechanism to assist the feature encoding network in evaluating the degree of collateral circulation in the brain. By using a hybrid attention mechanism, additional guidance and regularization are provided to enhance the collateral circulation feature representation across multiple stages. Time dimension information is added to the input, and multiple feature-level fusion modules are designed in the multi-branch network. The first fusion module in the single-stage feature extraction network completes the fusion of deep and shallow vessel features in the single-branch network, followed by the multi-stage network feature fusion module, which achieves feature fusion for four stages. Tested on a dataset of multi-phase cranial CTA images, the accuracy rate exceeding 90.43%. The experimental results demonstrate that the addition of these modules can fully explore collateral vessel features, improve feature expression capabilities, and optimize the performance of deep learning network model.

Comparison of two computed tomography perfusion post-processing software to assess infarct volume in patients with acute ischemic stroke.

Objectives: We used two automated software commonly employed in clinical practice-Olea Sphere (Olea) and Shukun-PerfusionGo (PerfusionGo)-to compare the diagnostic utility and volumetric agreement of computed tomography perfusion (CTP)-predicted final infarct volume (FIV) with true FIV in patients with anterior-circulation acute ischemic stroke (AIS). Methods: In all, 122 patients with anterior-circulation AIS who met the inclusion and exclusion criteria were retrospectively enrolled and divided into two groups: intervention group (n = 52) and conservative group (n = 70), according to recanalization of blood vessels and clinical outcome (NIHSS) after different treatments. Patients in both groups underwent one-stop 4D-CT angiography (CTA)/CTP, and the raw CTP data were processed on a workstation using Olea and PerfusionGo post-processing software, to calculate and obtain the ischemic core (IC) and hypoperfusion (IC plus penumbra) volumes, hypoperfusion in the conservative group and IC in the intervention group were used to define the predicted FIV. The ITK-SNAP software was used to manually outline and measure true FIV on the follow-up non-enhanced CT or MRI-DWI images. Intraclass correlation coefficients (ICC), Bland-Altman, and Kappa analysis were used to compare the differences in IC and penumbra volumes calculated by the Olea and PerfusionGo software to investigate the relationship between their predicted FIV and true FIV. Results: The IC and penumbra difference between Olea and PerfusionGo within the same group (p < 0.001) was statistically significant. Olea obtained larger IC and smaller penumbra than PerfusionGo. Both software partially overestimated the infarct volume, but Olea significantly overestimated it by a larger percentage. ICC analysis showed that Olea performed better than PerfusionGo (intervention-Olea: ICC 0.633, 95%CI 0.439-0.771; intervention-PerfusionGo: ICC 0.526, 95%CI 0.299-0.696; conservative-Olea: ICC 0.623, 95%CI 0.457-0.747; conservative-PerfusionGo: ICC 0.507, 95%CI 0.312-0.662). Olea and PerfusionGo had the same capacity in accurately diagnosing and classifying patients with infarct volume <70 ml. Conclusion: Both software had differences in the evaluation of the IC and penumbra. Olea's predicted FIV was more closely correlated with the true FIV than PerfusionGo's prediction. Accurate assessment of infarction on CTP post-processing software remains challenging. Our results may have important practice implications for the clinical use of perfusion post-processing software.

Comparison of Three Algorithms for Predicting Infarct Volume in Patients with Acute Ischemic Stroke by CT Perfusion Software: Bayesian, CSVD, and OSVD.

This study aimed to compare the performance of the Bayesian probabilistic method, circular Singular Value Decomposition (cSVD), and oscillation index Singular Value Decomposition (oSVD) algorithms in Olea Sphere for predicting infarct volume in patients with acute ischemic stroke (AIS). Eighty-seven patients suffering from AIS with large vessel occlusion were divided into improvement and progression groups. The improvement group included patients with successful recanalization (TICI 2b-3) after thrombectomy or whose clinical symptoms improved after thrombolysis. The progression group consisted of patients whose clinical symptoms did not improve or even got worse. The infarct core volume from the Olea Sphere software was used as the predicted infarct volume (PIV) in the improvement group, whereas the hypoperfusion volume was used as the PIV in the progression group. We defined predicted difference (PD) as PIV minus final infarct volume (FIV) measured at follow-up imaging. Differences among the three algorithms were assessed by the Friedman test. Spearman correlation analysis was used to verify the correlation between PIV and FIV. In addition, we performed a subgroup analysis of the progression group based on collateral circulation status. The median [interquartile range (IQR)] of the PD and Spearman correlation coefficients (SCCs) between PIV and FIV for the improvement group (n = 22) were: Bayesian = [6.99 (-14.72, 18.99), 0.500]; oSVD = [-12.74 (-41.06, -3.46), 0.423]; cSVD = [-15.38 (-38.92, -4.68), 0.586]. For the progression group (n = 65), the median (IQR) of PD and SCCs were: Bayesian = [1.00 (-34.07, 49.37), 0.748]; oSVD = [-0.17 (-53.42, 29.73), 0.712]; cSVD = [66.55 (7.94, 106.32), 0.674]. The Bayesian algorithm in the Olea Sphere software predicted infarct volumes with better accuracy and stability than the other two algorithms in both the progression and improvement groups.

A clinical-radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study.

OBJECTIVE: To build a clinical-radiomics model based on noncontrast computed tomography images to identify the risk of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT). MATERIALS AND METHODS: A total of 517 consecutive patients with AIS were screened for inclusion. Datasets from six hospitals were randomly divided into a training cohort and an internal cohort with an 8:2 ratio. The dataset of the seventh hospital was used for an independent external verification. The best dimensionality reduction method to choose features and the best machine learning (ML) algorithm to develop a model were selected. Then, the clinical, radiomics and clinical-radiomics models were developed. Finally, the performance of the models was measured using the area under the receiver operating characteristic curve (AUC). RESULTS: Of 517 from seven hospitals, 249 (48%) had HT. The best method for choosing features was recursive feature elimination, and the best ML algorithm to build models was extreme gradient boosting. In distinguishing patients with HT, the AUC of the clinical model was 0.898 (95% CI 0.873-0.921) in the internal validation cohort, and 0.911 (95% CI 0.891-0.928) in the external validation cohort; the AUC of radiomics model was 0.922 (95% CI 0.896-0.941) and 0.883 (95% CI 0.851-0.902), while the AUC of clinical-radiomics model was 0.950 (95% CI 0.925-0.967) and 0.942 (95% CI 0.927-0.958) respectively. CONCLUSION: The proposed clinical-radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.

Pinning Stabilizer Design for Probabilistic Boolean Control Networks via Condensation Digraph.

This article investigates the design of pinning controllers for state feedback stabilization of probabilistic Boolean control networks (PBCNs), based on the condensation digraph method. First, two effective algorithms are presented to achieve state feedback stabilization of the considered system from the perspective of condensation digraph. One is to find the desired matrix, and the other is to search for the minimum number of pinned nodes and specific pinned nodes. Then, all the mode-independent pinning controllers can be designed based on the desired matrix and pinned nodes. Several examples are delineated to illustrate the validity of the main results.