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.

Effects of laparoscopic radical prostatectomy on wound infection of surgery in patients with prostate cancer: A meta-analysis.

This meta-analysis aims to comprehensively assess the impact of laparoscopic radical prostatectomy (LRP) on wound infection in patients with prostate cancer (PCa). A systematic search was conducted, from database inception to November 2023, in EMBASE, Google Scholar, Cochrane Library, PubMed, Wanfang and China National Knowledge Infrastructure databases for randomized controlled trials (RCTs) comparing LRP with open radical prostatectomy (ORP) in the treatment of PCa. Two researchers independently screened the literature, extracted data and conducted quality assessments based on pre-defined inclusion and exclusion criteria. Stata 17.0 software was employed for data analysis. Overall, 15 RCTs involving 1458 PCa patients were included. The analysis revealed the incidence of wound infection (odds ratio [OR] = 0.28, 95% confidence interval [CI] = 0.16-0.51, p < 0.001) and complications (OR = 0.27, 95% CI = 0.20-0.37, p < 0.001) was significantly lower in the LRP group compared to the ORP group. This study demonstrates that LRP in PCa patients can effectively reduce the incidence of wound infections and complications, indicating significant therapeutic efficacy and justifying its broader 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.

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.

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.

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.

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.

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.

Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach.

Background: Acute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6-40% of patients undergoing IVT experience Early Neurological Deterioration (END), significantly impacting treatment efficacy and patient prognosis. Objective: This study aimed to develop and validate a predictive model for END in AIS patients post rt-PA administration using the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach. Methods: In this retrospective cohort study, data from 531 AIS patients treated with intravenous alteplase across two hospitals were analyzed. LASSO regression was employed to identify significant predictors of END, leading to the construction of a multivariate predictive model. Results: Six key predictors significantly associated with END were identified through LASSO regression analysis: previous stroke history, Body Mass Index (BMI), age, Onset to Treatment Time (OTT), lymphocyte count, and glucose levels. A predictive nomogram incorporating these factors was developed, effectively estimating the probability of END post-IVT. The model demonstrated robust predictive performance, with an Area Under the Curve (AUC) of 0.867 in the training set and 0.880 in the validation set. Conclusion: The LASSO regression-based predictive model accurately identifies critical risk factors leading to END in AIS patients following IVT. This model facilitates timely identification of high-risk patients by clinicians, enabling more personalized treatment strategies and optimizing patient management and outcomes.

Development and validation of a nomogram predictive model for cerebral small vessel disease: a comprehensive retrospective analysis.

Background: Cerebral small vessel disease (CSVD) is a significant contributor to stroke, intracerebral hemorrhages, and vascular dementia, particularly in the elderly. Early diagnosis remains challenging. This study aimed to develop and validate a novel nomogram for the early diagnosis of cerebral small vessel disease (CSVD). We focused on integrating cerebrovascular risk factors and blood biochemical markers to identify individuals at high risk of CSVD, thus enabling early intervention. Methods: In a retrospective study conducted at the neurology department of the Affiliated Hospital of Hebei University from January 2020 to June 2022, 587 patients were enrolled. The patients were randomly divided into a training set (70%, n = 412) and a validation set (30%, n = 175). The nomogram was developed using multivariable logistic regression analysis, with variables selected through the Least Absolute Shrinkage and Selection Operator (LASSO) technique. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA). Results: Out of 88 analyzed biomarkers, 32 showed significant differences between the CSVD and non-CSVD groups. The LASSO regression identified 12 significant indicators, with nine being independent clinical predictors of CSVD. The AUC-ROC values of the nomogram were 0.849 (95% CI: 0.821-0.894) in the training set and 0.863 (95% CI: 0.810-0.917) in the validation set, indicating excellent discriminative ability. Calibration plots demonstrated good agreement between predicted and observed probabilities in both sets. DCA showed that the nomogram had significant clinical utility. Conclusions: The study successfully developed a nomogram predictive model for CSVD, incorporating nine clinical predictive factors. This model offers a valuable tool for early identification and risk assessment of CSVD, potentially enhancing clinical decision-making and patient outcomes.

Disorder-tunable entanglement at infinite temperature.

Emerging quantum technologies hold the promise of unravelling difficult problems ranging from condensed matter to high-energy physics while, at the same time, motivating the search for unprecedented phenomena in their setting. Here, we use a custom-built superconducting qubit ladder to realize non-thermalizing states with rich entanglement structures in the middle of the energy spectrum. Despite effectively forming an "infinite" temperature ensemble, these states robustly encode quantum information far from equilibrium, as we demonstrate by measuring the fidelity and entanglement entropy in the quench dynamics of the ladder. Our approach harnesses the recently proposed type of non-ergodic behavior known as "rainbow scar," which allows us to obtain analytically exact eigenfunctions whose ergodicity-breaking properties can be conveniently controlled by randomizing the couplings of the model without affecting their energy. The on-demand tunability of quantum correlations via disorder allows for in situ control over ergodicity breaking, and it provides a knob for designing exotic many-body states that defy thermalization.