AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks.

Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.

Accurate Cancer Screening and Prediction of PD-L1-Guided Immunotherapy Efficacy Using Quantum Dot Nanosphere Self-Assembly and Machine Learning.

Accurate quantification of exosomal PD-L1 protein in tumors is closely linked to the response to immunotherapy, but robust methods to achieve high-precision quantitative detection of PD-L1 expression on the surface of circulating exosomes are still lacking. In this work, we developed a signal amplification approach based on aptamer recognition and DNA scaffold hybridization-triggered assembly of quantum dot nanospheres, which enables bicolor phenotyping of exosomes to accurately screen for cancers and predict PD-L1-guided immunotherapeutic effects through machine learning. Through DNA-mediated assembly, we utilized two aptamers for simultaneous ultrasensitive detection of exosomal antigens, which have synergistic roles in tumor diagnosis and treatment prediction, and thus, we achieved better sample classification and prediction through machine-learning algorithms. With a drop of blood, we can distinguish between different cancer patients and healthy individuals and predict the outcome of immunotherapy. This approach provides valuable insights into the development of personalized diagnostics and precision medicine.

Rupture-Related Features of Cerebral Arteriovenous Malformations and Their Utility in Predicting Hemorrhage.

BACKGROUND: Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage. METHODS: This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets. RESULTS: A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively. CONCLUSIONS: The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations.

Switchable Pickering Emulsions Stabilized via Synergistic Nanoparticles-Superamphiphiles Effects and Rapid Response to CO2/N2.

A CO2/N2-responsive emulsion provides milder reaction conditions, nontoxicity, and economic feasibility compared to other switchable surfactants. In this study, CO2/N2-responsive pickering emulsions were fabricated by using a compounded dispersion containing SiO2 nanoparticles (NPs) and superamphiphiles as the emulsifying agents. The synergistic effects of the SiO2 NPs and superamphiphiles significantly stabilized the emulsion at all of the tested concentrations and prevented complete phase separation of oil and water. The electrostatic interaction between the SiO2 NPs and superamphiphiles was disrupted after bubbling with CO2 for 30 s, resulting in the breaking of the emulsion. However, the dispersion recovered its interfacial activity after the introduction of N2 and again emulsified the emulsion. This reversible switching behavior was validated through three consecutive cycles of bubbling CO2/N2. The protonation and deprotonation of the SiO2 NPs and superamphiphiles in response to CO2/N2 facilitated reversible assembly and disassembly, which enabled the switching of the emulsions between inactive and active forms. The novel highly stable Pickering emulsions demonstrated rapid demulsification and emulsification in response to CO2/N2 and are promising for a wide range of applications.

Advances in rapid point-of-care virus testing.

Outbreaks of viral diseases seriously jeopardize people's health and cause huge economic losses. At the same time, virology provides a new perspective for biology, molecular biology and cancer research, and it is important to study the discovered viruses with potential applications. Therefore, the development of immediate and rapid viral detection methods for the prevention and treatment of viral diseases as well as the study of viruses has attracted extensive attention from scientists. With the continuous progress of science and technology, especially in the field of bioanalysis, a series of new detection techniques have been applied to the on-site rapid detection of viruses, which has become a powerful approach for human beings to fight against viruses. In this paper, the latest research progress of rapid point-of-care detection of viral nucleic acids, antigens and antibodies is presented. In addition, the advantages and disadvantages of these technologies are discussed from the perspective of practical application requirements. Finally, the problems and challenges faced by rapid viral detection methods and their development prospects are discussed.

In-plane anisotropic and ultra-low-loss polaritons in a natural van der Waals crystal.

Polaritons-hybrid light-matter excitations-enable nanoscale control of light. Particularly large polariton field confinement and long lifetimes can be found in graphene and materials consisting of two-dimensional layers bound by weak van der Waals forces1,2 (vdW materials). These polaritons can be tuned by electric fields3,4 or by material thickness5, leading to applications including nanolasers6, tunable infrared and terahertz detectors7, and molecular sensors8. Polaritons with anisotropic propagation along the surface of vdW materials have been predicted, caused by in-plane anisotropic structural and electronic properties9. In such materials, elliptic and hyperbolic in-plane polariton dispersion can be expected (for example, plasmon polaritons in black phosphorus9), the latter leading to an enhanced density of optical states and ray-like directional propagation along the surface. However, observation of anisotropic polariton propagation in natural materials has so far remained elusive. Here we report anisotropic polariton propagation along the surface of α-MoO3, a natural vdW material. By infrared nano-imaging and nano-spectroscopy of semiconducting α-MoO3 flakes and disks, we visualize and verify phonon polaritons with elliptic and hyperbolic in-plane dispersion, and with wavelengths (up to 60 times smaller than the corresponding photon wavelengths) comparable to those of graphene plasmon polaritons and boron nitride phonon polaritons3-5. From signal oscillations in real-space images we measure polariton amplitude lifetimes of 8 picoseconds, which is more than ten times larger than that of graphene plasmon polaritons at room temperature10. They are also a factor of about four larger than the best values so far reported for phonon polaritons in isotopically engineered boron nitride11 and for graphene plasmon polaritons at low temperatures12. In-plane anisotropic and ultra-low-loss polaritons in vdW materials could enable directional and strong light-matter interactions, nanoscale directional energy transfer and integrated flat optics in applications ranging from bio-sensing to quantum nanophotonics.

Structural basis of ubiquitin modification by the Legionella effector SdeA.

Protein ubiquitination is a multifaceted post-translational modification that controls almost every process in eukaryotic cells. Recently, the Legionella effector SdeA was reported to mediate a unique phosphoribosyl-linked ubiquitination through successive modifications of the Arg42 of ubiquitin (Ub) by its mono-ADP-ribosyltransferase (mART) and phosphodiesterase (PDE) domains. However, the mechanisms of SdeA-mediated Ub modification and phosphoribosyl-linked ubiquitination remain unknown. Here we report the structures of SdeA in its ligand-free, Ub-bound and Ub-NADH-bound states. The structures reveal that the mART and PDE domains of SdeA form a catalytic domain over its C-terminal region. Upon Ub binding, the canonical ADP-ribosyltransferase toxin turn-turn (ARTT) and phosphate-nicotinamide (PN) loops in the mART domain of SdeA undergo marked conformational changes. The Ub Arg72 might act as a 'probe' that interacts with the mART domain first, and then movements may occur in the side chains of Arg72 and Arg42 during the ADP-ribosylation of Ub. Our study reveals the mechanism of SdeA-mediated Ub modification and provides a framework for further investigations into the phosphoribosyl-linked ubiquitination process.

Mulches assist degraded soil recovery via stimulating biogeochemical cycling: metagenomic analysis.

Soil degradation of urban greening has caused soil fertility loss and soil organic carbon depletion. Organic mulches are made from natural origin materials, and represent a cost-effective and environment-friendly remediation method for urban greening. To reveal the effects of organic mulch on soil physicochemical characteristics and fertility, we selected a site that was covered with organic mulch for 6 years and a nearby lawn-covered site. The results showed that soil organic matter, total nitrogen, and available phosphorus levels were improved, especially at a depth of 0-20 cm. The activities of cellulase, invertase, and dehydrogenase in soil covered with organic mulch were 17.46%, 78.98%, and 283.19% higher than those under lawn, respectively. The marker genes of fermentation, aerobic respiration, methanogenesis, and methane oxidation were also enriched in the soil under organic mulch. Nitrogen cycling was generally repressed by the organic mulch, but the assimilatory nitrate and nitrite reduction processes were enhanced. The activity of alkaline phosphatase was 12.63% higher in the mulch-covered soil, and functional genes involved in phosphorus cycling were also enriched. This study presents a comprehensive investigation of the influence of organic mulch on soil microbes and provides a deeper insight into the recovery strategy for soil degradation following urban greening. KEY POINTS: • Long-term cover with organic mulches assists soil recovery from degradation • Soil physical and chemical properties were changed by organic mulches • Organic mulches enhanced genes involved in microbially mediated C and P cycling • Soil organic matter was derived from decomposition of organic mulch and carbon fixation • N cycling was repressed by mulches, except for assimilatory NO2- and NO3- reductions.

Light consistency correction for the liquid crystal tunable filter hyperspectral imaging system.

In hyperspectral images, every pixel encompasses continuous spectral information. Compared with traditional colorimeters, using hyperspectral imaging systems (HIS) for fabric color measurement can result in obtaining richer color information. However, measuring fabric colors with liquid crystal tunable filter HIS can lead to challenges related to light consistency. In this paper, we adopted an innovative approach, integrating gradient boosted decision trees with a sliding window algorithm to develop a uniformity calibration model addressing the illumination uniformity issue. To address the consistency issues across various light sources, we further adopted a deep neural network (DNN) model to correct the reflectance measurements under different light sources. Subsequently, this model was merged with the uniformity calibration model to form a light-consistency correction model. Through calibration, we successfully reduced the color difference of the corrected samples from 3.636 to 0.854, an enhancement of 76.51%. This means that after calibration we can achieve consistency in fabric color measurements under nonuniform lighting and different light sources.

Progressive coronal caudal curve after corrective osteotomies for congenital cervicothoracic scoliosis: incidence and predictors.

OBJECTIVE: Postoperative progressive coronal caudal curve (PCC) was characterized by a postoperative de novo caudal S-curve ≥ 20° following congenital cervicothoracic scoliosis (CTS) corrective osteotomies, and at least 20° greater than the preoperative measurement, while the incidence was uncertain and the pathogenesis was equivocal. The objective of this study was to investigate the morbidity and potential factors contributing to PCC following CTS surgery. METHODS: This study reviewed 72 CTS patients between 2005 and 2021. Patients were categorized into two groups according to the absence or presence of PCC at last follow-up, namely the nonprogressive curve group (NPC-group) and the progressive curve group (PC-group). Demographics, radiographic data and the Scoliosis Research Society-22 (SRS-22) questionnaire results were reviewed. Multivariate linear regression analyses were utilized to determine possible predictors for PCC. RESULTS: PCC was observed in 11 (15%) of the total 72 patients. Compared with the NPC-group, the PC-group exhibited greater postoperative residual local curve (24.0 ± 9.7° vs. 9.1 ± 4.4°, P < 0.001), upper instrumented vertebra (UIV) tilt (16.9 ± 7.4° vs. 6.2 ± 3.7°, P < 0.001), T1 tilt (14.3 ± 9.4° vs. 6.6 ± 3.9°, P = 0.022) and neck tilt (10.1 ± 6.7° vs. 3.7 ± 2.5, P = 0.009). The multivariable linear regression demonstrated that the larger postoperative UIV tilt, residual local curve and neck tilt were associated with PCC. In addition, patients with PCC showed lower SRS-22 scores in terms of pain, mental health, self-image and satisfaction (P < 0.05). CONCLUSIONS: The morbidity of PCC was 15% in CTS patients who underwent corrective osteotomies. Greater residual local curve, postoperative UIV tilt and neck tilt were identified as predictors for PCC.

Sol-Gel Synthesized Amorphous (InxGa1-x)2O3 for UV Photodetection with High Responsivity.

ß-Ga2O3 photodetectors have the advantages of low dark current and strong radiation resistance in UV detection. However, the limited photocurrent has restricted their applications. Herein, MSM UV photodetectors based on (InxGa1-x)2O3 (x = 0, 0.1, 0.2, 0.3) by a sol-gel method were fabricated and studied. The doping of indium ions in Ga2O3 leads to lattice distortion and promotes the formation of oxygen vacancies. The oxygen vacancies in (InxGa1-x)2O3 can be modulated by various proportions of indium, and the increased oxygen vacancies contribute to the enhancement of electron concentration. The results show that the amorphous In0.4Ga1.6O3 photodetector exhibited improved performances, including a high light-to-dark current ratio (2.8 × 103) and high responsivity (739.2 A/W). This work provides a promising semiconductor material In0.4Ga1.6O3 for high-performance MSM UV photodetectors.

Retrograde placement of flow diversion for the treatment of giant internal carotid artery aneurysm.

The use of flow diverters has been well-validated for the treatment of giant internal carotid artery aneurysms. However, in certain complex cases, the navigation of stent microcatheters across the neck may pose a relative challenge.1-3 In this technical video (video 1), we present the case of a patient in their 50s experiencing discomfort in the left eye. Angiography identified a giant aneurysm in the ophthalmic segment of the left internal carotid artery. Before seeking care at our institution, the patient had two interventional procedures, both unsuccessful due to difficulties in navigating the microcatheter past the aneurysm neck.4-5 In our management, after multiple unsuccessful anterograde attempts, we employed a retrograde strategy via the vertebral-basilar-posterior communicating artery route. This approach facilitated the successful deployment of the flow diverter and led to effective aneurysm embolization, underscoring the value of retrograde techniques for challenging cases.

Dendrimer-Encapsulated Halide Perovskite Nanocrystals for Self-Powered White Light-Emitting Glass.

Perovskite nanocrystals (PNCs) have attracted substantial attention due to their inspiring intrinsic merits such as low cost, high performance, and solution processability, but when it comes to the usage of blends of different colored PNCs with the purpose of covering the broadband spectrum field, the high degree of instability remains a major bottleneck. Herein, we report a family of dendritic ammonium ligands that act as stiff shell-encapsulating PNCs for improving their stability and suppressing ion permeability in mixed colloidal PNC solutions. The as-synthesized ligand-encapsulated PNCs notably achieve near-unity photoluminescence quantum yields (PLQYs) and strongly resist the unwanted ion exchange reaction under aggressive anion source attack. To fabricate self-powered white-emitting glass, the stabilized mixed colored PNCs were embedded into the laminated glass, which simultaneously acted as absorbers-emitters for luminescent solar concentrators (LSCs) and emitters for white light-emitting glass.

Real-Time Imaging of Single Viral mRNA Translation in Live Cells Using CRISPR/dCas13.

Translation is one of the many critical cellular activities regulated by viruses following host-cell invasion, and studies of viral mRNA translation kinetics and subcellular localization require techniques for the dynamic, real-time visualization of translation. However, conventional tools for imaging mRNA translation often require coding region modifications that may affect native translation. Here, we achieve dynamic imaging of translation with a tool that labels target mRNAs with unmodified coding regions using a CRISPR/dCas13 system with specific complementary paired guide RNAs. This system enables a real-time dynamic visualization of the translation process and is a promising tool for further investigations of the mechanisms of translation.

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study.

BACKGROUND: Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers. METHODS: A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models. RESULTS: The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I-II or WHO III-IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively. CONCLUSIONS: This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas.

Estimation of venous sinus pressure drop in patients with idiopathic intracranial hypertension using 4D-flow MRI.

OBJECTIVE: We aimed to explore a non-invasive estimate of pressure drop in patients who undergo venous sinus stenting to treat idiopathic intracranial hypertension (IIH). METHODS: This prospective study included 28 IIH patients scheduled for venous stenting. 4D-flow MRI was acquired 24-48 h before venous manometry. Manometry-obtained pressure drop (Mp) was dichotomized into low (Lp: 0-8 mmHg) and high (Hp: 8-30 mmHg) groups. Hemodynamic indices were compared between Lp and Hp. Trans-stenotic pressure drop was estimated by work-energy equation, simplified Bernoulli equation, vorticity magnitude, and velocity difference between inlet and outlet and was compared with Mp. Measurement agreement, correlation, and accuracy were evaluated using the κ coefficient, Pearson's r, and confusion matrix-derived accuracy. RESULTS: Among 28 patients (mean age 38.8 ± 12.7), 19 (67.9%) were female. Work-energy equation-estimated pressure drop (WEp) had strong correlation (r = 0.91, 95% confidence interval [CI]: 0.81-0.96, p < 0.001) and high agreement (intraclass correlation coefficient = 0.90, 95% CI: 0.78-0.95, p < 0.001) with Mp. WEp classified Lp and Hp with an accuracy of 0.96. The κ value between WEp and Mp was 0.92 (95% CI: 0.78-1.00). In the work-energy equation, the viscosity energy term (Ve) had the largest weights, and the ratio of Ve to the summation of the three energy terms was 0.93 ± 0.07. Ve had strong correlation with mVort (r = 0.93, 95% CI: 0.85-0.97, p < 0.001), and mean vorticity magnitude was significantly elevated in Hp compared to that in Lp (259.8 vs. 174.9 mL/s, p < 0.001). CONCLUSION: Trans-stenotic pressure drop in IIH can be estimated using the work-energy equation with favorable accuracy. KEY POINTS: • Trans-stenotic pressure drop in patients with idiopathic intracranial hypertension can be estimated accurately with the work-energy equation using the 4D-flow MRI full velocity field. • Compared with traditional venous sinus manometry, the 4D-flow MRI-derived pressure drop is totally non-invasive and cost-saving. • 4D-flow MRI may help neurointerventionalist to select IIH patients suitable for venous sinus stenting.

Mediative Mechanism of Freezing/Thawing on Greenhouse Gas Emissions in an Inland Saline-Alkaline Wetland: a Metagenomic Analysis.

Inland saline-alkaline wetlands distributed in the mid-high latitude have repeatedly experienced freezing and thawing. However, the response of greenhouse gas (GHG) emission and microbially-mediated carbon and nitrogen cycle to freezing and thawing remains unclear. We monitored the GHG flux in an inland saline-alkaline wetland and found that, compared with the growth period, the average CO2 flux decreased from 171.99 to 76.61-80.71 mg/(m2‧h), the average CH4 flux decreased from 10.72 to 1.96-3.94 mg/(m2‧h), and the average N2O flux decreased from 56.17 to - 27.14 to - 20.70 µg/(m2‧h). Freezing and thawing significantly decreased the relative abundance of functional genes involved in carbon and nitrogen cycles. The aceticlastic methanogenic pathway was the main methanogenic pathway, whereas the Candidatus Methylomirabilis oxyfera was the most abundant methane oxidizer in the wetland. Ammonia-oxidizing archaea and denitrifier belonging to proteobacteria was the major microbial N2O source, while bacteria within clade II nosZ was the major microbial N2O sink. Freezing and thawing reduced the relative abundance of these genes, leading to a decrease in GHG flux.

Methylome, transcriptome, and phenotype changes induced by temperature conditions experienced during sexual reproduction in Fragaria vesca.

Temperature conditions experienced during embryogenesis and seed development may induce epigenetic changes that increase phenotypic variation in plants. Here we investigate if embryogenesis and seed development at two different temperatures (28 vs. 18°C) result in lasting phenotypic effects and DNA methylation changes in woodland strawberry (Fragaria vesca). Using five European ecotypes from Spain (ES12), Iceland (ICE2), Italy (IT4), and Norway (NOR2 and NOR29), we found statistically significant differences between plants from seeds produced at 18 or 28°C in three of four phenotypic features investigated under common garden conditions. This indicates the establishment of a temperature-induced epigenetic memory-like response during embryogenesis and seed development. The memory effect was significant in two ecotypes: in NOR2 flowering time, number of growth points and petiole length were affected, and in ES12 number of growth points was affected. This indicates that genetic differences between ecotypes in their epigenetic machinery, or other allelic differences, impact this type of plasticity. We observed statistically significant differences between ecotypes in DNA methylation marks in repetitive elements, pseudogenes, and genic elements. Leaf transcriptomes were also affected by embryonic temperature in an ecotype-specific manner. Although we observed significant and lasting phenotypic change in at least some ecotypes, there was considerable variation in DNA methylation between individual plants within each temperature treatment. This within-treatment variability in DNA methylation marks in F. vesca progeny may partly be a result of allelic redistribution from recombination during meiosis and subsequent epigenetic reprogramming during embryogenesis.

High-resolution magnetic resonance imaging-based radiomic features aid in selecting endovascular candidates among patients with cerebral venous sinus thrombosis.

OBJECTIVES: Cerebral venous sinus thrombosis (CVST) can cause sinus obstruction and stenosis, with potentially fatal consequences. High-resolution magnetic resonance imaging (HRMRI) can diagnose CVST qualitatively, although quantitative screening methods are lacking for patients refractory to anticoagulation therapy and who may benefit from endovascular treatment (EVT). Thus, in this study, we used radiomic features (RFs) extracted from HRMRI to build machine learning models to predict response to drug therapy and determine the appropriateness of EVT. MATERIALS AND METHODS: RFs were extracted from three-dimensional T1-weighted motion-sensitized driven equilibrium (MSDE), T2-weighted MSDE, T1-contrast, and T1-contrast MSDE sequences to build radiomic signatures and support vector machine (SVM) models for predicting the efficacy of standard drug therapy and the necessity of EVT. RESULTS: We retrospectively included 53 patients with CVST in a prospective cohort study, among whom 14 underwent EVT after standard drug therapy failed. Thirteen RFs were selected to construct the RF signature and CVST-SVM models. In the validation dataset, the sensitivity, specificity, and area under the curve performance for the RF signature model were 0.833, 0.937, and 0.977, respectively. The radiomic score was correlated with days from symptom onset, history of dyslipidemia, smoking, fibrin degradation product, and D-dimer levels. The sensitivity, specificity, and area under the curve for the CVST-SVM model in the validation set were 0.917, 0.969, and 0.992, respectively. CONCLUSIONS: The CVST-SVM model trained with RFs extracted from HRMRI outperformed the RF signature model and could aid physicians in predicting patient responses to drug treatment and identifying those who may require EVT.

Carbon nanocoils decorated with scale-like mesoporous NiO nanosheets for ultrasensitive room temperature ppb-level NO2 sensing.

Although NO2 detection based on metal oxide semiconductors (MOSs) has received continuous attention, the sensing properties of MOSs still need to be further improved for practical application. Carbon nanocoils (CNCs) exhibit excellent physicochemical properties due to their unique polycrystalline amorphous structure and helical morphology. Herein, CNCs composited with NiO nanosheets were developed for room temperature NO2 sensing, in which highly dispersed mesoporous NiO nanosheets on CNCs was achieved with extremely higher sensitivity. Due to a large number of exposed active sites and the efficient conductive network of CNCs, this particular scale-like nanocomposite exhibits a nearly 8 times higher response to 60 ppm NO2 than bare NiO nanosheets at room temperature, outperforming the majority of previously reported NO2 sensors at room temperature. Moreover, the nanocomposite-based gas sensor has a detection concentration limit of 60.3 ppb, which is advantageous for the sensing of NO2 at low concentrations. We believe that this work will provide the direction for the research and development of highly sensitive smart sensors, as well as encourage further investigation of multifunctional sensing applications utilizing CNCs as the primary frame support material.