Achieving Ultra-Narrow-Band Deep-Red Electroluminescence By a Soliton-type Dye Squaraine.

Due to the soliton-like electronic structural characteristics, cyanine dyes typically exhibit spectral behaviors such as large molar extinction coefficients, narrow spectra, and high fluorescence efficiency. However, their extensive applications as emitters in electroluminescence are largely ignored due to their serious emission quenching in the aggregation state. Herein, it is reported a squaraine dye (a type of cyanine) SQPhEt. At different solution concentrations, the unusual decrease in full-width at half-maxima (FWHM) with increasing Stokes shift indicates the fluorescence quenching of SQPhEt in the aggregated state is because of the strong self-absorption effect. A sensitized device structure can help to reduce the doping concentration of dye, which can effectively suppress self-absorption. Benefitting from the large molar extinction coefficient of SQPhEt, even at low doping concentrations of 0.1 wt%, efficient Förster energy transfer can be achieved. The corresponding spin-coating sensitized device based on SQPhEt as the dopant exhibits favorable deep-red emission at 668 nm with a small FWHM of 0.10 eV.

MOSP: A user-interface package for simulating metal nanoparticle's structure and reactivity under operando conditions.

The structures of metal nanoparticles (NPs) significantly influence their catalytic reactivities. Recent in situ experimental observations of dramatic structural changes in NPs underscore the need to establish a dynamic structure-property relationship that accounts for the reconstruction of NPs in reactive environments. Here, we present the MOSP, a free and open-source graphical user interface (GUI) package designed to simulate the structure and reactivity of metal NPs under operando conditions. MOSP integrates two models: the multiscale structure reconstruction model predicting equilibrium metal NP structures under specific reaction conditions and the kinetic Monte Carlo model simulating the reaction dynamics. This combination allows for the exploration of the dynamic structure-property relationships of NPs. MOSP enhances user accessibility through its intuitive GUI, facilitating easy input, post-processing, and visualization of simulation data. This article is the release note of MOSP, focusing on its implementation and functionality.

Doping bilayer hole-transport polymer strategy stabilizing solution-processed green quantum-dot light-emitting diodes.

Quantum-dot light-emitting diodes (QLEDs) are solution-processed electroluminescence devices with great potential as energy-saving, large-area, and low-cost display and lighting technologies. Ideally, the organic hole-transport layers (HTLs) in QLEDs should simultaneously deliver efficient hole injection and transport, effective electron blocking, and robust electrochemical stability. However, it is still challenging for a single HTL to fulfill all these stringent criteria. Here, we demonstrate a general design of doping-bilayer polymer-HTL architecture for stabilizing high-efficiency QLEDs. We show that the bilayer HTLs combining the electrochemical-stable polymer and the electron-blocking polymer unexpectedly increase the hole injection barrier. We mitigated the problem by p-doping of the underlying sublayer of the bilayer HTLs. Consequently, green QLEDs with an unprecedented maximum luminance of 1,340,000 cd m-2 and a record-long operational lifetime (T95 lifetime at an initial luminance of 1000 cd m-2 is 17,700 hours) were achieved. The universality of the strategy is examined in various polymer-HTL systems, providing a general route toward high-performance solution-processed QLEDs.

Targeted Delivery of Quinoxaline-Based Semiconducting Polymers for Tumor Photothermal Therapy.

Photothermal therapy (PTT) holds great potential in the field of cancer treatment due to its high specificity and low invasiveness. However, the low conversion efficiency, inadequate tumor accumulation, and limited cellular uptake continue to impede PTT effectiveness in treating tumors. The present study focuses on the utilization of quinoxaline and its nanoparticles to develop an organic semiconducting photothermal agent (PAQI-BDTT) for tumor photothermal therapy. To achieve this, PAQI-BDTT was encapsulated within liposomes modified with cyclic Arg-Gly-Asp (cRGD) peptide targeting tumors (named T-BDTT-Lipo). Notably, T-BDTT-Lipo demonstrated a positive photothermal conversion efficiency of 74% when exposed to an 808 nm laser, along with NIR-II fluorescence imaging capabilities. The efficacy of T-BDTT-Lipo in tumor tissue accumulation and precise targeting of malignant cells has been confirmed through both in vitro and in vivo experiments guided by fluorescence imaging. Under single dose and 808 nm light irradiation, T-BDTT-Lipo generated local intracellular hyperthermia at the tumor site. The elevated temperature additionally exerted a significant inhibitory effect on tumor growth and recurrence, thereby extending the survival duration of mice harboring tumors. The therapeutic nanosystem (T-BDTT-Lipo) proposed in this work demonstrates the enormous potential of semiconducting photothermal agents in photothermal therapy, laying the foundation for the next clinical application.

The roles of branched-chain amino acids metabolism in tumorigenesis and progression.

Branched-chain amino acids (BCAAs), including leucine, valine, and isoleucine, play crucial roles in regulating metabolic balance and maintaining physiological functions in the body. Extensive studies have been focused on their implications in obesity, diabetes, and cardiovascular diseases. Nevertheless, accumulating evidence suggests that BCAAs metabolism also plays significant roles in tumorigenesis and progression. In this review, we overview recent progress of the study on BCAAs metabolism including its relationship with epigenetic regulation. Particularly, we discuss the metabolic reprogramming and metabolic sensing of BCAAs and its intermediate metabolites in tumor cells and microenvironment to decipher their functions. An enhanced understanding of the roles and mechanism of BCAAs metabolism in tumorigenesis and progression will contribute to development of novel therapeutic strategies against tumor.

Fast simulation for interacting four-level Rydberg atoms: electromagnetically induced transparency and Autler-Townes splitting.

Quantum sensing using Rydberg atoms is an emerging technology for precise measurement of electric fields. However, most existing computational methods are all based on a single-particle model and neglect Rydberg-Rydberg interaction between atoms. In this study, we introduce the interaction term into the conventional four-level optical Bloch equations. By incorporating fast iterations and solving for the steady-state solution efficiently, we avoid the computation of a massive 4N × 4N dimensional matrix. Additionally, we apply the Doppler frequency shift to each atom used in the calculation, eliminating the requirement for an additional Doppler iteration. These schemes allow for the calculation of the interaction between 7000 atoms around one minute. Based on the many-body model, we investigate the Rydberg-Rydberg interaction of Rydberg atoms under different atomic densities. Furthermore, we compare our results with the literature data of a three-level system and the experimental results of our own four-level system. The results demonstrate the validity of our model, with an effective error of 4.59% compared to the experimental data. Finally, we discover that the many-body model better predicts the linear range for measuring electric fields than the single-particle model, making it highly applicable in precise electric field measurements.

Low-dose pegylated recombinant human granulocyte-colony stimulating factor as hematopoietic support for adjuvant chemotherapy in Chinese patients with breast cancer: An open-label, randomized, non-inferiority trial.

AIMS: The recommended dosage of pegylated recombinant human granulocyte-colony stimulating factor (PEG-rhG-CSF) for Western chemotherapy patients is 6 mg per cycle. However, for Eastern Asians, the optimal dose remains unknown. METHODS: This open-label, randomized, non-inferiority trial (NCT05283616) enrolled Chinese female breast cancer patients receiving adjuvant chemotherapy. Participants were randomized to receive either 3 or 6 mg of PEG-rhG-CSF per cycle, stratified by body weight (BW; ≤60 kg vs. >60 kg). The primary endpoint was timely absolute neutrophil count (ANC) recovery before the second cycle of chemotherapy. RESULTS: A total of 122 patients were randomized and 116 were included for efficacy analyses. The timely ANC recovery rate in the 3 mg arm was 89.8%, compared to 93.0% in the 6 mg arm (one-sided 95% confidence interval [CI] lower limit for difference: -11.7%), meeting the prespecified non-inferiority margin of 15%. The rate was 93.3% with PEG-rhG-CSF 3 mg and 96.6% with 6 mg in patients with BW ≤ 60 kg, and 86.2% and 89.3%, respectively, in those with BW > 60 kg. Although the incidence of severe neutropenia was similar across arms, the occurrence of excessively high ANC and white blood cell counts was higher in the 6 mg arm. No grade ≥3 adverse events related to PEG-rhG-CSF occurred. CONCLUSION: Three milligrams of PEG-rhG-CSF per cycle provided non-inferior neutrophil protection and attenuated neutrophil overshoot compared to 6 mg doses. This low-dose regimen could be a new supportive care option for Chinese breast cancer patients receiving anthracycline-based adjuvant chemotherapy.

Enhancing Early Lung Cancer Diagnosis: Predicting Lung Nodule Progression in Follow-Up Low-Dose CT Scan with Deep Generative Model.

Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam.

Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening.

Purpose To evaluate the feasibility of leveraging serial low-dose CT (LDCT) scans to develop a radiomics-based reinforcement learning (RRL) model for improving early diagnosis of lung cancer at baseline screening. Materials and Methods In this retrospective study, 1951 participants (female patients, 822; median age, 61 years [range, 55-74 years]) (male patients, 1129; median age, 62 years [range, 55-74 years]) were randomly selected from the National Lung Screening Trial between August 2002 and April 2004. An RRL model using serial LDCT scans (S-RRL) was trained and validated using data from 1404 participants (372 with lung cancer) containing 2525 available serial LDCT scans up to 3 years. A baseline RRL (B-RRL) model was trained with only LDCT scans acquired at baseline screening for comparison. The 547 held-out individuals (150 with lung cancer) were used as an independent test set for performance evaluation. The area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to assess the performances of the models in the classification of screen-detected nodules. Results Deployment to the held-out baseline scans showed that the S-RRL model achieved a significantly higher test AUC (0.88 [95% CI: 0.85, 0.91]) than both the Brock model (AUC, 0.84 [95% CI: 0.81, 0.88]; P = .02) and the B-RRL model (AUC, 0.86 [95% CI: 0.83, 0.90]; P = .02). Lung cancer risk stratification was significantly improved by the S-RRL model as compared with Lung CT Screening Reporting and Data System (NRI, 0.29; P < .001) and the Brock model (NRI, 0.12; P = .008). Conclusion The S-RRL model demonstrated the potential to improve early diagnosis and risk stratification for lung cancer at baseline screening as compared with the B-RRL model and clinical models. Keywords: Radiomics-based Reinforcement Learning, Lung Cancer Screening, Low-Dose CT, Machine Learning © RSNA, 2024 Supplemental material is available for this article.

Dynamic Active Sites In Situ Formed in Metal Nanoparticle Reshaping under Reaction Conditions.

Understanding the nonequilibrium transformation of nanocatalysts under reaction conditions is important because metastable atomic structures may be created during the process, which offers unique activities in reactions. Although reshaping of metal nanoparticles (NPs) under reaction conditions has been widely recognized, the dynamic reshaping process has been less studied at the atomic scale. Here, we develop an atomistic kinetic Monte Carlo model to simulate the complete reshaping process of Pt nanoparticles in a CO environment and reveal the in situ formation of atomic clusters on the NP surface, a new type of active site beyond conventional understanding, boosting the reactivities in the CO oxidation reaction. Interestingly, highly active peninsula and inactive island clusters both form on the (111) facets and interchange in varying states of dynamic equilibrium, which influences the catalytic activities significantly. This study provides new fundamental knowledge of nanocatalysis and new guidance for the rational design of nanocatalysts.