Pharmacological activity, phytochemistry, and organ protection of lithospermic acid.

Lithospermic acid (LA) is a water-soluble phenolic acid compound extracted and separated from the dried root and the rhizome of Salviamiltiorrhiza Bge (Labiatae), possessing multiple biological activities. Firstly, in terms of pharmacological activities, LA has been proven to possess anti-inflammatory, antioxidant, autophagy activation, and antiapoptotic properties. Secondly, the pharmacokinetic characteristics of LA show rapid and extensive distribution in various tissues after intravenous administration, followed by rapid elimination and excretion. Additionally, potential therapeutic effects of LA have been found in various diseases such as thrombosis, Parkinson's disease, hepatitis B, diabetes, and psoriasis, among others. Particularly, LA has shown promising prospects in the treatment of clinical heart diseases and has been included in new drug formulations for the treatment of chronic angina, demonstrating superior efficacy compared to current cardiovascular drugs. In conclusion, this review comprehensively introduces the pharmacological mechanisms, pharmacokinetics, and protective effects in diseases of LA. These information can lay a theoretical foundation for the future development and new clinical applications of LA.

Monolithically integrated photonic crystal surface emitters on silicon with a vortex beam by using bound states in the continuum.

Optical resonant cavities with high quality factor (Q-factor) are widely used in science and technology for their capabilities of strong confinement of light and enhanced light-matter interaction. The 2D photonic crystal structure with bound states in the continuum (BICs) is a novel concept for resonators with ultra-compact device size, which can be used to generate surface emitting vortex beams based on symmetry-protected BICs at the Γ point. Here, to the best of our knowledge, we demonstrate the first photonic crystal surface emitter with a vortex beam by using BICs monolithically grown on CMOS-compatible silicon substrate. The fabricated quantum-dot BICs-based surface emitter operates at 1.3 µm under room temperature (RT) with a low continuous wave (CW) optically pumped condition. We also reveal the BIC's amplified spontaneous emission with the property of a polarization vortex beam, which is promising to provide a novel degree of freedom in classical and quantum realms.

Mild Synthesis of Polychlorinated Arenes for Efficient Organic Light-emitting Diodes with Dual Thermally Activated Delayed Fluorescence.

Polychlorinated (hetero)arenes have shown great promise for organic optoelectronics applications. However, the harsh synthetic routes for polychlorinated compounds and the possible luminescence quenching from the compact intermolecular π-π stacking induced by chlorine atoms limit their investigations and applications in luminescent materials. Herein, two isomeric polychlorinated polycyclic aromatic hydrocarbon (PAH) compounds JY-1-Cl and JY-2-Cl consisting of rigidified aryl ketones and amine are designed and synthesized under mild conditions through nucleophilic chlorination intermediated by an electron donor-acceptor complex. Among them, as a result of the strong π-π interactions induced by chlorine atoms, JY-2-Cl exhibits bright monomer and dimer emissions with dual thermally activated delayed fluorescence (TADF) characters. Notably, compared with the non-chlorinated compounds, a high photoluminescence quantum yield is maintained after introducing multiple chlorine atoms into JY-2-Cl. The first dual-TADF organic light-emitting diodes are also successfully fabricated with maximum external quantum efficiency as high as 29.1 % by employing JY-2-Cl as emitter. This work presents a new paradigm and synthesis of polychlorinated amine-carbonyl PAHs and demonstrates the great potential of the chlorinated materials for luminescent applications.

Methyl butyrate attenuates concanavalin A-induced autoimmune hepatitis by inhibiting Th1-cell activation and homing to the liver.

Con A-induced hepatitis is the most commonly used animal model for simulating autoimmune hepatitis (AIH). In this study, we investigated whether methyl butyrate (MB) alleviates Con A-induced hepatitis and how it affects Con A-stimulated lymphocytes. MB improves liver function in AIH mice, reducing the expression of several inflammatory cytokines and Th1 cell-associated chemokines in the liver, while significantly inhibiting toll-like receptor signaling pathway. Also in the liver, we verified that infiltrating Th1 cells were fewer after MB treatment. In vitro, we found that the activation of both human and mouse Th1 cells by Con A were inhibited by MB and the human-derived cells were even more sensitive. And MB caused a reduction in IFN-γ secretion together with TNF-α and IL-6. The above findings suggest that MB inhibits the activation and homing of Th1 cells to the liver, thereby attenuating Con A-induced liver injury, and may be a potential therapeutic agent for AIH.

Molecular Engineering of Sulfur-Bridged Polycyclic Emitters Towards Tunable TADF and RTP Electroluminescence.

Highly efficient organic thermally activated delayed fluorescence (TADF) and room-temperature phosphorescence (RTP) emitters for organic light-emitting diodes (OLEDs) generally consist of a twisted donor-acceptor skeleton with aromatic amine donors. Herein, through introducing sulfur atoms into isomeric pentaphene and pentacene frameworks, we demonstrate a set of polycyclic luminophores exhibiting efficient TADF and RTP characters. The incorporation of sulfur atoms confirms a folded molecular plane, while intensifies singlet-triplet spin-orbit coupling. Further, the isomeric effect has a significant effect on the electronic structure of excited state, giving rise to the investigated compounds tunable luminescence mechanisms of TADF and RTP. With efficient triplet harvesting ability, maximum external quantum efficiencies up to 25.1 % and 8.7 % are achieved for the corresponding TADF and RTP OLEDs, verifying the great potential of sulfur-bridged frameworks for highly efficient devices.

Integrated bioinformatic analysis revealed biological processes and immune cells implicated in autoimmune hepatitis.

Autoimmune hepatitis (AIH) is an immune-mediated inflammatory liver disease for which the pathogenesis remains incompletely understood. The current study aimed to reveal key biological processes and immune cells implicated in AIH by integrated bioinformatic analysis. The global gene expression in livers from wild-type BALB/c mice, mice with Tgfb1 deficiency, and mice with both Tgfb1 and Ifng deficiency was assessed by microarray data analysis. Differentially expressed genes were identified and subjected to functional enrichment analysis. AIH mice with Tgfb1 deletion showed significantly enhanced immune responses but impaired metabolic processes, whereas increased T cell activation and cytokine production, but weakened organic acid and lipid metabolic processes were observed in mice with deletion of both Tgfb1 and Ifng. In addition, infiltration of immune cells was evaluated by CIBERSORT. Increased infiltration of T cells, macrophages, and natural killer cells, and decreased infiltration of neutrophils, eosinophils, plasma cells, and B cells were observed in AIH mice. In conclusion, we identified potential biological processes and immune cells that contributed to AIH; further investigations are needed to confirm these findings and thus provide a potential novel therapeutic target for AIH treatment.

TNIP1-mediated TNF-α/NF-κB signalling cascade sustains glioma cell proliferation.

As a malignant tumour of the central nervous system, glioma exhibits high incidence and poor prognosis. Although TNIP1 and the TNF-α/NF-κB axis play key roles in immune diseases and inflammatory responses, their relationship and role in glioma remain unknown. Here, we revealed high levels of TNIP1 and TNF-α/NF-κB in glioma tissue. Glioma cell proliferation was activated with TNF-α treatment and showed extreme sensitivity to the TNF receptor antagonist. Furthermore, loss of TNIP1 disbanded the A20 complex responsible for IκB degradation and NF-κB nucleus translocation, and consequently erased TNFα-induced glioma cell proliferation. Thus, our investigation uncovered a vital function of the TNIP1-mediated TNF-α/NF-κB axis in glioma cell proliferation and provides novel insight into glioma pathology and diagnosis.

A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing.

Environmental sensing is a key technology for the development of unmanned cars, drones and robots. Many vision sensors cannot work normally in an environment with insufficient light, and the cost of using multiline LiDAR is relatively high. In this paper, a novel and inexpensive visual navigation sensor based on structured-light vision is proposed for environment sensing. The main research contents of this project include: First, we propose a laser-stripe-detection neural network (LSDNN) that can eliminate the interference of reflective noise and haze noise and realize the highly robust extraction of laser stripes region. Then we use a gray-gravity approach to extract the center of laser stripe and used structured-light model to reconstruct the point clouds of laser center. Then, we design a single-line structured-light sensor, select the optimal parameters for it and build a car-platform for experimental evaluation. This approach was shown to be effective in our experiments and the experimental results show that this method is more accurate and robust in complex environment.

Association between Clinical and Ultrasonic Characteristics of Varicocele and Lower Extremity Varicose Vein in Men.

BACKGROUND: The study investigated the association between lower extremity varicose veins in men and varicocele. METHODS: A total of 100 patients who presented to the Department of Vascular Surgery in the First Affiliated Hospital of Anhui Medical University with the diagnosis of lower extremity varicose veins were included in the study group. A total of 100 adults without vascular disease were included as controls. The prevalence of varicocele between the study group and the control group was compared. We compared the prevalence of varicocele and the mean diameter of spermatic veins between the patients with and without reflux in the saphenofemoral junctions. We analyzed the association between the maximum spermatic vein diameter and the maximum diameter of lower extremity varicose veins in patients who had both lower extremity varicose vein and varicocele in the study group. According to their clinic signs, patients with lower extremity varicose veins were divided into C1-C6 by clinic sign grade of Clinical-Etiology-Anatomy-Pathophysiology (CEAP), and we investigated the trend of the incidence of varicocele and the mean diameter of spermatic veins in different grades. RESULTS: The patients with lower extremity varicose veins had a statistically significant (χ2 = 20.05, P < 0.01) higher rate of varicocele when compared with controls. We compared the prevalence of varicocele and the mean diameter of spermatic veins between the patients with and without reflux in the saphenofemoral junctions and found no statistically significant differences between them (prevalence of varicocele P > 0.05, diameter P > 0.05). We found a linear correlation between the maximum spermatic vein diameter and the maximum diameter of lower extremity varicose veins in the patients who had both lower extremity varicose veins and varicocele in the study group (coefficient of rank correlation r = 0.4072, P < 0.01). The patients in the study group were classified into 6 grades by CEAP. After the analysis by trend chi-square, we found that the prevalence of varicocele had no statistical trend in different grades (χ2 = 0.8798, P > 0.05), and the mean diameter of spermatic vein also had no statistical trend in different grades (F = 1.59, P > 0.05). CONCLUSIONS: In conclusion, we demonstrated that the prevalence of varicocele is higher in patients with varicose veins in lower extremity than the patients without vascular diseases. The reason for the association between varicose vein in lower extremity and varicocele remains uncertain.

Prediction of CD8+T lymphocyte infiltration levels in gastric cancer from contrast-enhanced CT and clinical factors using machine learning.

BACKGROUND: CD8+ T lymphocyte infiltration is closely associated with the prognosis and immunotherapy response of gastric cancer (GC). For now, the examination of CD8 infiltration levels relies on endoscopic biopsy, which is invasive and unsuitable for longitude assessment during anti-tumor therapy. PURPOSE: This work aims to develop and validate a noninvasive workflow based on contrast-enhanced CT (CECT) images to evaluate the CD8+ T-cell infiltration profiles of GC. METHODS: GC patients were retrospectively and consecutively enrolled and randomly assigned to the training (validation) or test cohort at a 7:3 ratio. All patients were binary classified into the CD8-high (infiltrated proportion ≥ 20%) or CD8-low group (infiltrated proportion < 20%) group. A total of 1170 radiomics features were extracted from each presurgical CECT series. After feature selection, fifteen radiomics features were transmitted to three independent machine-learning models for the computation of predictive radiological scores. Multilayer perceptron (MLP) was applied to merge the radiological scores with clinical factors. The predictive efficacy of the radiological scores and of the combined model was evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis in both the training and test cohorts. RESULTS: A total of 210 patients were enrolled in this study (mean age: 63.22 ± 8.74 years, 151 men), and were randomly assigned to the training set (n = 147) or the test set (n = 63). The merged radiological score was correlated with CD8 infiltration in both the training (p = 1.8e-10) and test cohorts (p = 0.00026). The combined model integrating the radiological scores and clinical features achieved an area under the curve (AUC) value of 0.916 (95% CI: 0.872-0.960) in the training set and 0.844 (95% CI: 0.742-0.946) in the test set for classifying CD8-high GCs. The model was well-calibrated and exhibited net benefit over "treat-all" and"treat-none" strategies in decision curve analysis. CONCLUSIONS: Artificial intelligent systems combining radiological features and clinical factors could accurately predict CD8 infiltration levels of GC, which may benefit personalized treatment of GC in the context of immunotherapy.

Comparing animal well-being between bile duct ligation models.

A prevailing animal model currently used to study severe human diseases like obstructive cholestasis, primary biliary or sclerosing cholangitis, biliary atresia, and acute liver injury is the common bile duct ligation (cBDL). Modifications of this model include ligation of the left hepatic bile duct (pBDL) or ligation of the left bile duct with the corresponding left hepatic artery (pBDL+pAL). Both modifications induce cholestasis only in the left liver lobe. After induction of total or partial cholestasis in mice, the well-being of these animals was evaluated by assessing burrowing behavior, body weight, and a distress score. To compare the pathological features of these animal models, plasma levels of liver enzymes, bile acids, bilirubin, and within the liver tissue, necrosis, fibrosis, inflammation, as well as expression of genes involved in the synthesis or transport of bile acids were assessed. The survival rate of the animals and their well-being was comparable between pBDL+pAL and pBDL. However, surgical intervention by pBDL+pAL caused confluent necrosis and collagen depositions at the edge of necrotic tissue, whereas pBDL caused focal necrosis and fibrosis in between portal areas. Interestingly, pBDL animals had a higher survival rate and their well-being was significantly improved compared to cBDL animals. On day 14 after cBDL liver aspartate, as well as alanine aminotransferase, alkaline phosphatase, glutamate dehydrogenase, bile acids, and bilirubin were significantly elevated, but only glutamate dehydrogenase activity was increased after pBDL. Thus, pBDL may be primarily used to evaluate local features such as inflammation and fibrosis or regulation of genes involved in bile acid synthesis or transport but does not allow to study all systemic features of cholestasis. The pBDL model also has the advantage that fewer mice are needed, because of its high survival rate, and that the well-being of the animals is improved compared to the cBDL animal model.

CT-based radiomics model for predicting perineural invasion status in gastric cancer.

PURPOSE: Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients. METHODS: This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models. RESULTS: A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility. CONCLUSION: We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

Highly Horizontal Oriented Tricomponent Exciplex Host with Multiple Reverse Intersystem Crossing Channels for High-Performance Narrowband Electroluminescence and Eye-Protection White Organic Light-Emitting Diodes.

Despite multiple-resonance thermally activated delayed fluorescence (MR-TADF) emitters with small full-width at half maximum are attractive for wide color-gamut display and eye-protection lighting applications, their inefficient reverse intersystem crossing (RISC) process and long exciton lifetime induce serious efficiency roll-off, which significantly limits their development. Herein, a novel device concept of building highly efficient tricomponent exciplex with multiple RISC channels is proposed to realize reduced exciton quenching and enhanced upconversion of nonradiative triplet excitons, and subsequently used as a host for high-performance MR-TADF organic light-emitting diodes (OLEDs). Compared with traditional binary exciplex, the tricomponent exciplex exhibits obviously improved photoluminescence quantum yield, emitting dipole orientation and RISC rate constant, and a record-breaking external quantum efficiency (EQE) of 30.4% is achieved for tricomponent exciplex p-PhBCzPh: PO-T2T: DspiroAc-TRZ (50: 20: 30) based OLED. Remarkably, maximum EQEs of 36.2% and 40.3% and ultralow efficiency roll-off with EQEs of 26.1% and 30.0% at 1000 cd m-2 are respectively achieved for its sky-blue and pure-green MR-TADF doped OLEDs. Additionally, the blue emission unit hosted by tricomponent exciplex is combined with an orange-red TADF emission unit to achieve a double-emission-layer blue-hazard-free warm white OLED with an EQEmax of 30.3% and stable electroluminescence spectra over a wide brightness range.

BBLN: A bilateral-branch learning network for unknown protein-protein interaction prediction.

Unknown Protein-Protein Interactions (PPIs) prediction has a huge demand in the biological analysis field. Since the effect of the limited availability of protein data is severe, transferable representations are highly demanded to be learned from various data. The latest works enhance the model performance on unknown PPIs prediction and have achieved certain improvements by combining protein information and relation information on PPI graph. However, such methods inevitably suffer from a so-called information monotonicity problem that limits the improvements when encountering large amounts of unknown PPIs. The prediction performance cannot be actually increased without considering the complementary information and relationship information among various modalities of protein data. To this end, we propose a bilateral-branch learning network to deeply enhance the both complementary and relationship information based on the amino acid sequence and gene ontology from multi- and cross-modal views. Experimental results on massive real-world datasets show that our method significantly outperforms the previous state-of-the-art on both traditional and novel unknown PPIs prediction.

SPINT2 is involved in the proliferation, migration and phenotypic switching of aortic smooth muscle cells: Implications for the pathogenesis of thoracic aortic dissection.

Thoracic aortic dissection (TAD) is a severe and extremely dangerous cardiovascular disease. Proliferation, migration and phenotypic switching of vascular smooth muscle cells (SMCs) are major pathogenetic mechanisms involved in the development of TAD. The present study was designed to investigate the expression and potential function of serine peptidase inhibitor Kunitz type 2 (SPINT2) in TAD. The gene expression profile data for ascending aorta from patients with TAD were downloaded from the GEO database with the accession number GSE52093. Bioinformatics analysis using GEO2R indicated that the differentially expressed SPINT2 was prominently decreased in TAD. The expression levels of SPINT2 mRNA and protein in aortic dissection specimens and normal aorta tissues were measured using reverse transcription-quantitative PCR and western blotting. SPINT2 expression was downregulated in clinical samples from aortic dissection specimens of patients with TAD compared with the corresponding expression noted in tissues derived from patients without TAD. In vitro, platelet-derived growth factor BB (PDGF-BB) was applied to induce the isolated primary mouse aortic SMC phenotypic modulation (a significant upregulation in the expression levels of synthetic markers), and the SMCs were infected with the adenoviral vector, Ad-SPINT2, to construct SPINT2-overexpressed cell lines. SMC viability was detected by an MTT assay and SMC proliferation was detected via the presence of Ki-67-positive cells (immunofluorescence staining). To explore the effects of SPINT2 on SMC migration, a wound healing assay was conducted. ELISA and western blotting assays were used to measure the content and expression levels of MMP-2 and MMP-9. The expression levels of vimentin, collagen I, α-SMA and SM22α were measured using western blotting. The PDGF-BB-induced proliferation and migration of SMCs were recovered by SPINT2 overexpression. The increase in the expression levels of SPINT2 reduced the expression levels of active matrix metalloproteinases (MMPs), MMP-2 and MMP-9. Overexpression of SPINT2 suppressed SMC switching from a contractile to a synthetic type, as evidenced by decreased vimentin and collagen I expression levels along with increased α-smooth muscle actin and smooth muscle protein 22-α expression levels. Furthermore, activation of ERK was inhibited in SPINT2-overexpressing SMCs. A specific ERK agonist, 12-O-tetradecanoylphorbol-13-acetate, reversed the SPINT2-mediated inhibition of SMC migration and the phenotypic switching. Collectively, the data indicated that SPINT2 was implicated in the proliferation, migration and phenotypic switching of aortic SMCs, suggesting that it may be involved in TAD progression.

Quantitative Radiological Features and Deep Learning for the Non-Invasive Evaluation of Programmed Death Ligand 1 Expression Levels in Gastric Cancer Patients: A Digital Biopsy Study.

RATIONALE AND OBJECTIVES: Programmed Death-Ligand 1 (PD-L1) is an important biomarker for patient selection of immunotherapy in gastric cancer (GC). This study aimed to construct and validate a non-invasive virtual biopsy system based on radiological features and clinical factors to predict the PD-L1 expression level in GC. MATERIALS AND METHODS: 217 patients who received gastrectomy for GC were consecutively enrolled in this study, with 157 patients from center 1 as the training cohort and 60 patients from center 2 as the external validation cohort. 1205 quantitative radiomics features were extracted from preprocessed pre-operative contrast-enhanced CT images of enrolled patients. A radiological signature was computed using a regression random forest model and was integrated with clinical factors in a multilayer perceptron. The performance of the digital biopsy system was evaluated by the receiver operating characteristic (ROC) curve and calibration curve in both the training and validation cohort. RESULTS: 15 features were selected for the construction of radiological signature, which was significantly associated with expression levels of PD-L1 in both the training cohort (p<0.0001) and the external validation cohort (p<0.01). The hybrid deep learning model integrating the radiological signature and clinical factor could accurately distinguish GCs with high PD-L1 expression levels in both the training cohort (AUC = 0.806, 95%CI: 0.736-0.875) and the validation cohort (AUC = 0.784, 95%CI: 0.668-0.901). CONCLUSIONS: Our results indicate that the combination of deep learning and quantitative radiological features are potential approaches for the non-invasive evaluation of PD-L1 expression levels in GC. The digital biopsy system could provide valuable suggestive information for clinical decision-making of immunotherapy in GC.

Highly Efficient Purely Organic Phosphorescence Light-Emitting Diodes Employing a Donor-Acceptor Skeleton with a Phenoxaselenine Donor.

Purely organic room-temperature phosphorescence (RTP) materials generally exhibit low phosphorescence quantum yield (ϕP ) and long phosphorescence lifetime (τP ) due to the theoretically spin-forbidden triplet state. Herein, by introducing a donor-acceptor (D-A) skeleton with a phenoxaselenine donor, three nonaromatic amine donor containing compounds with high ϕP and short τP in amorphous films are developed. Besides the enhanced spin-orbit coupling (SOC) by the heavy-atom effect of selenium, the D-A skeleton which facilitates orbital angular momentum change can further boost SOC, and severe nonradiative energy dissipation is also suppressed by the rigid molecular structure. Consequently, a record-high external quantum efficiency of 19.5% are achieved for the RTP organic light-emitting diode (OLED) based on 2-(phenoxaselenin-3-yl)-4,6-diphenyl-1,3,5-triazine (PXSeDRZ). Moreover, voltage-dependent color-tunable emission and single-molecule white emission are also realized. These results shed light on the broad prospects of purely organic phosphorescence materials as highly efficient OLED emitters especially for potential charming lighting applications.

A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics.

OBJECTIVES: This study aims to develop and validate a virtual biopsy model to predict microsatellite instability (MSI) status in preoperative gastric cancer (GC) patients based on clinical information and the radiomics of deep learning algorithms. METHODS: A total of 223 GC patients with MSI status detected by postoperative immunohistochemical staining (IHC) were retrospectively recruited and randomly assigned to the training (n = 167) and testing (n = 56) sets in a 3:1 ratio. In the training set, 982 high-throughput radiomic features were extracted from preoperative abdominal dynamic contrast-enhanced CT (CECT) and screened. According to the deep learning multilayer perceptron (MLP), 15 optimal features were optimized to establish the radiomic feature score (Rad-score), and LASSO regression was used to screen out clinically independent predictors. Based on logistic regression, the Rad-score and clinically independent predictors were integrated to build the clinical radiomics model and visualized as a nomogram and independently verified in the testing set. The performance and clinical applicability of hybrid model in identifying MSI status were evaluated by the area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve (DCA). RESULTS: The AUCs of the clinical image model in training set and testing set were 0.883 [95% CI: 0.822-0.945] and 0.802 [95% CI: 0.666-0.937], respectively. This hybrid model showed good consistency in the calibration curve and clinical applicability in the DCA curve, respectively. CONCLUSIONS: Using preoperative imaging and clinical information, we developed a deep-learning-based radiomics model for the non-invasive evaluation of MSI in GC patients. This model maybe can potentially support clinical treatment decision making for GC patients.

Confining donor conformation distributions for efficient thermally activated delayed fluorescence with fast spin-flipping.

Fast spin-flipping is the key to exploit the triplet excitons in thermally activated delayed fluorescence based organic light-emitting diodes toward high efficiency, low efficiency roll-off and long operating lifetime. In common donor-acceptor type thermally activated delayed fluorescence molecules, the distribution of dihedral angles in the film state would have significant influence on the photo-physical properties, which are usually neglected by researches. Herein, we find that the excited state lifetimes of thermally activated delayed fluorescence emitters are subjected to conformation distributions in the host-guest system. Acridine-type flexible donors have a broad conformation distribution or bimodal distribution, in which some conformers feature large singlet-triplet energy gap, leading to long excited state lifetime. Utilization of rigid donors with steric hindrance can restrict the conformation distributions in the film to achieve degenerate singlet and triplet states, which is beneficial to efficient reverse intersystem crossing. Based on this principle, three prototype thermally activated delayed fluorescence emitters with confined conformation distributions are developed, achieving high reverse intersystem crossing rate constants greater than 106 s-1, which enable highly efficient solution-processed organic light-emitting diodes with suppressed efficiency roll-off.

Automatic Gray Image Coloring Method Based on Convolutional Network.

Image coloring is a time-consuming and laborious work. For a work, color collocation is an important factor to determine its quality. Therefore, automatic image coloring is a topic with great research significance and application value. With the development of computer hardware, deep learning technology has achieved satisfactory results in the field of automatic coloring. According to the source of color information, this paper can divide automatic coloring methods into three types: image coloring based on prior knowledge, image coloring based on reference pictures, and interactive coloring. The coloring method can meet the needs of most users, but there are disadvantages such as users cannot get the multiple objects in a picture of different reference graph coloring. Aiming at this problem, based on the instance of color image segmentation and image fusion technology, the use of deep learning is proposed to implement regional mixed color more and master the method. It can be divided into foreground color based on reference picture and background color based on prior knowledge. In order to identify multiple objects and background areas in the image and fuse the final coloring results together, a method of image coloring based on CNN is proposed in this paper. Firstly, CNN is used to extract their semantic information, respectively. According to the extractive semantic information, the color of the designated area of the reference image is transferred to the designated area of the grayscale image. During the transformation, images combined with semantic information are input into CNN model to obtain the content feature map of grayscale image and the style feature map of reference image. Then, a random noise map is iterated to make the noise map approach the content feature map as a whole and the specific target region approach the designated area of the style feature map. Experimental results show that the proposed method has good effect on image coloring and has great advantages in network volume and coloring effect.