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.

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.

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.

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.

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.

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.

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.

Prediction of tumor recurrence by distinct immunoprofiles in liver transplant patients based on mass cytometry.

Recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT) is a marker of poor prognosis. However, the reliable biomarkers of post-LT HCC recurrence remain to be identified. In this study, serial peripheral blood samples from the LT recipients with and without HCC recurrence were collected at five time points. Single-cell mass cytomertry (CyTOF) was utilized for the in-depth analysis of peripheral blood monocellular cells (PBMCs). CyTOF analysis showed that at 3 weeks post-LT, the activated immune cell population was increased, while the fraction of immune cells with suppressive functions (myeloid-derived suppressive cells) was reduced. The post-LT immune composition in patients with LT for HCC was enormously different from that in patients with LT for causes other than HCC. Furthermore, at 3 weeks after LT, compared with patients without recurrence, the patients with HCC recurrences were high in two subsets of T cells: CD57+ HLA-DR+ CD8+ and CD28+γδ. The CD57+ HLA-DR+ CD8+ T cells presented high levels of perforin, granzyme B, and Ki-67 and displayed a highly cytotoxic and proliferative phenotype, while the CD28+γδ T cells had reduced levels of IFN-γ and, hence, were less activated compared to CD28- cells. Based on these findings, we concluded that analyzing the PBMCs of LT recipients by CyTOF can predict the post-LT HCC recurrence. The distinct immune features can stratify patients with the risk of HCC recurrence at 3 weeks after LT, which will help clinician in further management plan and improve the prognosis of patients.

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.

Intrahepatic transcriptomics reveals gene signatures in chronic hepatitis B patients responded to interferon therapy.

Chronic hepatitis B virus (HBV) infection remains a substantial public health burden worldwide. Alpha-interferon (IFNα) is one of the two currently approved therapies for chronic hepatitis B (CHB), to explore the mechanisms underlying IFNα treatment response, we investigated baseline and 24-week on-treatment intrahepatic gene expression profiles in 21 CHB patients by mRNA-seq. The data analyses demonstrated that PegIFNα treatment significantly induced antiviral responses. Responders who achieved HBV DNA loss and HBeAg or HBsAg seroconversion displayed higher fold change and larger number of up-regulated interferon-stimulated genes (ISGs). Interestingly, lower expression levels of certain ISGs were observed in responders in their baseline biopsy samples. In HBeAg+ patients, non-responders had relative higher baseline HBeAg levels than responders. More importantly, HBeAg- patients showed higher HBsAg loss rate than HBeAg+ patients. Although a greater fold change of ISGs was observed in HBeAg- patients than HBeAg+ patients, upregulation of ISGs in HBeAg+ responders exceeded HBeAg- responders. Notably, PegIFNα treatment increased monocyte and mast cell infiltration, but decreased CD8 T cell and M1 macrophage infiltration in both responders and non-responders, while B cell infiltration was increased only in responders. Moreover, co-expression analysis identified ribosomal proteins as critical players in antiviral response. The data also indicate that IFNα may influence the production of viral antigens associated with endoplasmic reticulum. Collectively, the intrahepatic transcriptome analyses in this study enriched our understanding of IFN-mediated antiviral effects in CHB patients and provided novel insights into the development of potential strategies to improve IFNα therapy.

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.

3D-printed lightweight dorsal skin fold chambers from PEEK reduce chamber-related animal distress.

The dorsal skinfold chamber is one of the most important in vivo models for repetitive longitudinal assessment of microcirculation and inflammation. This study aimed to refine this model by introducing a new lightweight chamber made from polyetheretherketone (PEEK). Body weight, burrowing activity, distress, faecal corticosterone metabolites and the tilting angle of the chambers were analysed in mice carrying either a standard titanium chamber or a PEEK chamber. Data was obtained before chamber preparation and over a postoperative period of three weeks. In the early postoperative phase, reduced body weight and increased faecal corticosterone metabolites were found in mice with titanium chambers. Chamber tilting and tilting-related complications were reduced in mice with PEEK chambers. The distress score was significantly increased in both groups after chamber preparation, but only returned to preoperative values in mice with PEEK chambers. In summary, we have shown that light chambers reduce animal distress and may extend the maximum dorsal skinfold chamber observation time. Chambers made of PEEK are particularly suitable for this purpose: They are autoclavable, sufficiently stable to withstand rodent bites, inexpensive, and widely available through 3D printing.

A "Flexible" Purely Organic Molecule Exhibiting Strong Spin-Orbital Coupling: Toward Nondoped Room-Temperature Phosphorescence OLEDs.

Purely organic materials usually exhibit weak spin-orbital coupling (SOC) effect because of the lack of noble heavy metals, and the generation and direct emission from the triplet state is spin-forbidden. This would lead to slow intersystem crossing, long triplet lifetime, and low phosphorescence quantum yield. Herein, strong spin-orbital coupling between singlet and triplet was observed in a "flexible" and twist thianthrene-pyrimidine-based purely organic compound in an amorphous film state, which shows a fast intersystem crossing process and a high phosphorescence rate of 1.1 × 103 s-1. The heavy atom sulfur and nitrogen atoms in the molecule can provide n-π* transition character for efficient spin-orbital coupling. Moreover, the flexible molecule skeleton enables conformational change and molecular vibration in excited states, which was proved to be vital for efficient vibrational spin-orbital coupling. Benefitting from the strong SOC effect, a nondoped purely organic phosphorescence light-emitting diode was fabricated, which achieves a maximum external quantum efficiency of 7.98%, corresponding to an exciton utilization ratio exceeding 87.6%.

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.

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms.

BACKGROUND AND OBJECTIVE: Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological classification of breast lesions. Recent advancements in deep learning have made the analyses faster and more accurate. This study aims to develop a deep learning model architecture for breast cancer mass detection and segmentation using the mammography. METHODS: In this work we proposed a double shot model for mass detection and segmentation simultaneously using a combination of YOLO (You Only Look Once) and LOGO (Local-Global) architectures. Firstly, we adopted YoloV5L6, the state-of-the-art object detection model, to position and crop the breast mass in mammograms with a high resolution; Secondly, to balance training efficiency and segmentation performance, we modified the LOGO training strategy to train the whole images and cropped images on the global and local transformer branches separately. The two branches were then merged to form the final segmentation decision. RESULTS: The proposed YOLO-LOGO model was tested on two independent mammography datasets (CBIS-DDSM and INBreast). The proposed model performs significantly better than previous works. It achieves true positive rate 95.7% and mean average precision 65.0% for mass detection on CBIS-DDSM dataset. Its performance for mass segmentation on CBIS-DDSM dataset is F1-score=74.5% and IoU=64.0%. The similar performance trend is observed in another independent dataset INBreast as well. CONCLUSIONS: The proposed model has a higher efficiency and better performance, reduces computational requirements, and improves the versatility and accuracy of computer-aided breast cancer diagnosis. Hence it has the potential to enable more assistance for doctors in early breast cancer detection and treatment, thereby reducing mortality.

Glycosyltransferases: Mining, engineering and applications in biosynthesis of glycosylated plant natural products.

UDP-Glycosyltransferases (UGTs) catalyze the transfer of nucleotide-activated sugars to specific acceptors, among which the GT1 family enzymes are well-known for their function in biosynthesis of natural product glycosides. Elucidating GT function represents necessary step in metabolic engineering of aglycone glycosylation to produce drug leads, cosmetics, nutrients and sweeteners. In this review, we systematically summarize the phylogenetic distribution and catalytic diversity of plant GTs. We also discuss recent progress in the identification of novel GT candidates for synthesis of plant natural products (PNPs) using multi-omics technology and deep learning predicted models. We also highlight recent advances in rational design and directed evolution engineering strategies for new or improved GT functions. Finally, we cover recent breakthroughs in the application of GTs for microbial biosynthesis of some representative glycosylated PNPs, including flavonoid glycosides (fisetin 3-O-glycosides, astragalin, scutellarein 7-O-glucoside), terpenoid glycosides (rebaudioside A, ginsenosides) and polyketide glycosides (salidroside, polydatin).

Distress Analysis of Mice with Cervical Arteriovenous Fistulas.

The welfare of laboratory animals is a consistent concern for researchers. Its evaluation not only fosters ethical responsibility and addresses legal requirements, but also provides a solid basis for a high quality of research. Recently, a new cervical arteriovenous model was created in mice to understand the pathophysiology of arteriovenous fistula, which is the most commonly used access for hemodialysis. This study evaluates the distress caused by this new animal model. Ten male C57B6/J mice with cervical arteriovenous fistula were observed for 21 days. Non-invasive parameters, such as body weight, faecal corticosterone metabolites, burrowing activity, nesting activity and distress scores were evaluated at each time point. Six out of ten created arteriovenous fistula matured within the observation time as defined by an increased diameter. The body weight of all animals was reduced after surgery but recovered within five days. In addition, the distress score was significantly increased during the early time point but not at the late time point after arteriovenous fistula creation. Neither burrowing activity nor nesting behaviour were significantly reduced after surgical intervention. Moreover, faecal corticosterone metabolite concentrations did not significantly increase. Therefore, the cervical murine arteriovenous fistula model induced moderate distress in mice and revealed an appropriate maturation rate of the fistulas.

Boosting purely organic room-temperature phosphorescence performance through a host-guest strategy.

The host-guest doping system has aroused great attention due to its promising advantage in stimulating bright and persistent room-temperature phosphorescence (RTP). Currently, exploration of the explicit structure-property relationship of bicomponent systems has encountered obstacles. In this work, two sets of heterocyclic isomers showing promising RTP emissions in the solid state were designed and synthesized. By encapsulating these phosphors into a robust phosphorus-containing host, several host-guest cocrystalline systems were further developed, achieving highly efficient RTP performance with a phosphorescence quantum efficiency (ϕ P) of ∼26% and lifetime (τ P) of ∼32 ms. Detailed photophysical characterization and molecular dynamics (MD) simulation were conducted to reveal the structure-property relationships in such bicomponent systems. It was verified that other than restricting the molecular configuration, the host matrix could also dilute the guest to avoid concentration quenching and provide an external heavy atom effect for the population of triplet excitons, thus boosting the RTP performance of the guest.

Diagnostic Ability of Methods Depicting Distress of Tumor-Bearing Mice.

Subcutaneous tumor models in mice are the most commonly used experimental animal models in cancer research. To improve animal welfare and the quality of scientific studies, the distress of experimental animals needs to be minimized. For this purpose, one must assess the diagnostic ability of readout parameters to evaluate distress. In this study, we evaluated different noninvasive readout parameters such as body weight change, adjusted body weight change, faecal corticosterone metabolites concentration, burrowing activity and a distress score by utilising receiver operating characteristic curves. Eighteen immunocompromised NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ mice were used for this study; half were subcutaneously injected with A-375 cells (human malignant melanoma cells) that resulted in large tumors. The remaining mice were inoculated with SCL-2 cells (cutaneous squamous cell carcinoma cells), which resulted in small tumors. The adjusted body weight and faecal corticosterone metabolites concentration had a high diagnostic ability in distinguishing between mice before cancer cell injection and mice bearing large tumors. All other readout parameters had a low diagnostic ability. These results suggest that adjusted body weight and faecal corticosterone metabolites are useful to depict the distress of mice bearing large subcutaneous tumors.