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"Zeng, Qi"
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Real spectra, Anderson localization, and topological phases in one-dimensional quasireciprocal systems
We introduce the one-dimensional quasireciprocal lattices where the forward hopping amplitudes between nearest neighboring sites t + t jR are chosen to be a random permutation of the backward hopping t + t jL or vice versa. The values of t jL (or t jR ) can be periodic, quasiperiodic, or randomly distributed. We show that the Hamiltonian matrices are pseudo-Hermitian and the energy spectra are real as long as t jL (or t jR ) are smaller than the threshold value. While the non-Hermitian skin effect is always absent in the eigenstates due to the global cancellation of local nonreciprocity, the competition between the nonreciprocity and the accompanying disorders in hopping amplitudes gives rise to energy-dependent localization transitions. Moreover, in the quasireciprocal Su–Schrieffer–Heeger models with staggered hopping t jL (or t jR ), topologically nontrivial phases are found in the real-spectra regimes characterized by nonzero winding numbers. Finally, we propose an experimental scheme to realize the quasireciprocal models in electrical circuits. Our findings shed new light on the subtle interplay among nonreciprocity, disorder, and topology.
Journal Article
Manganese Oxide Nanoparticles As MRI Contrast Agents In Tumor Multimodal Imaging And Therapy
Contrast agents (CAs) play a crucial role in high-quality magnetic resonance imaging (MRI) applications. At present, as a result of the Gd-based CAs which are associated with renal fibrosis as well as the inherent dark imaging characteristics of superparamagnetic iron oxide nanoparticles, Mn-based CAs which have a good biocompatibility and bright images are considered ideal for MRI. In addition, manganese oxide nanoparticles (MONs, such as MnO, MnO
, Mn
O
, and MnO
) have attracted attention as T1-weighted magnetic resonance CAs due to the short circulation time of Mn(II) ion chelate and the size-controlled circulation time of colloidal nanoparticles. In this review, recent advances in the use of MONs as MRI contrast agents for tumor detection and diagnosis are reported, as are the advances in in vivo toxicity, distribution and tumor microenvironment-responsive enhanced tumor chemotherapy and radiotherapy as well as photothermal and photodynamic therapies.
Journal Article
Neuroinflammation Following Traumatic Brain Injury: Take It Seriously or Not
Traumatic brain injury (TBI) is associated with high mortality and disability, with a substantial socioeconomic burden. With the standardization of the treatment process, there is increasing interest in the role that the secondary insult of TBI plays in outcome heterogeneity. The secondary insult is neither detrimental nor beneficial in an absolute sense, among which the inflammatory response was a complex cascade of events and can thus be regarded as a double-edged sword. Therefore, clinicians should take the generation and balance of neuroinflammation following TBI seriously. In this review, we summarize the current human and animal model studies of neuroinflammation and provide a better understanding of the inflammatory response in the different stages of TBI. In particular, advances in neuroinflammation using proteomic and transcriptomic techniques have enabled us to identify a functional specific delineation of the immune cell in TBI patients. Based on recent advances in our understanding of immune cell activation, we present the difference between diffuse axonal injury and focal brain injury. In addition, we give a figurative profiling of the general paradigm in the pre- and post-injury inflammatory settings employing a bow-tie framework.
Journal Article
Preparation of Ultrafine W Powder via H2 Reduction of Carbon-Containing WO3: Influences of Reduction Temperature and C/WO3 Molar Ratio
Ultrafine W powder is a key material for manufacturing high-performance W-based products. In this study, ultrafine W powder was prepared via the H2 reduction of carbon-containing WO3, and the parameters of reduction temperature (740–830 °C) and C/WO3 molar ratio (0.5–2.5) were mainly considered. The results demonstrated that, with the increase in reduction temperature, the reaction rate gradually increased, while the particle size of W powder exhibited a trend showing an initial decrease and then increase, with a minimum value of 146 nm at 770 °C. The results also showed that, with the increase in C/WO3 molar ratio, the reaction rate gradually decreased, while the particle size of W powder also first decreased and then increased, with its minimum value at a C/WO3 molar ratio of 1.0. The reduction pathways of H2 reduction of WO3 to W was given as WO3→WO2.9→WO2.72→WO2→W. Due to the co-actions of nucleating agent and the synergistic reduction effect, the particle size of W powder obtained by reducing carbon-containing WO3 was smaller than that obtained by reducing pure WO3, and a possible reaction mechanism was proposed.
Journal Article
Diagnosis, prevalence, and outcomes of sarcopenia in kidney transplantation recipients: A systematic review and meta‐analysis
The prevalence of sarcopenia and its clinical predictors and clinical impact vary among kidney transplant recipients (KTRs), in part because of different diagnostic criteria. This study aimed to assess the reported diagnosis criteria of sarcopenia and compare them in terms of prevalence, clinical predictors, and impact of sarcopenia. The Medline, Embase, and Cochrane Library were searched for the full‐length reports published until 28 January 2022. The subgroup analysis, meta‐regression, and sensitivity analysis were performed and heterogeneity was assessed using the I2. A total of 681 studies were retrieved, among which only 23 studies (including 2535 subjects, 59.7% men, mean age 49.8 years) were eventually included in the final analysis. The pooled prevalence in these included studies was 26% [95% confidence interval (95% CI): 20–34%, I2 = 93.45%], including 22% (95% CI: 14–32%, I2 = 88.76%) in men and 27% (95% CI: 14–41%, I2 = 90.56%) in women (P = 0.554 between subgroups). The prevalence of sarcopenia diagnosed using low muscle mass was 34% (95% CI: 21–48%, I2 = 95.28%), and the prevalence of using low muscle mass in combination with low muscle strength and/or low physical performance was 21% (95% CI: 15–28%, I2 = 90.37%) (P = 0.08 between subgroups). In meta‐regression analyses, the mean age (regression coefficient: 1.001, 95% CI: 0.991–1.011) and percentage male (regression coefficient: 0.846, 95% CI: 0.367–1.950) could not predict the effect size. Lower body mass index (odds ratio (OR): 0.57, 95% CI: 0.39–0.84, I2 = 61.5%), female sex (OR: 0.31, 95% CI: 0.16–0.61, I2 = 0.0%), and higher age (OR: 1.08, 95% CI: 1.05–1.10, I2 = 10.1%) were significantly associated with a higher risk for sarcopenia in KTRs, but phase angle (OR: 0.81, 95% CI: 0.16–4.26, I2 = 84.5%) was not associated with sarcopenia in KTRs. Sarcopenia was not associated with rejections (risk ratio (RR): 0.67, 95% CI: 0.23–1.92, I2 = 12.1%), infections (RR: 1.03, 95% CI: 0.34–3.12, I2 = 87.4%), delayed graft functions (RR: 0.81, 95% CI: 0.46–1.43, I2 = 0.0%), and death (RR: 0.95, 95% CI: 0.32–2.82, I2 = 0.0%) in KRTs. Sarcopenia was found to be very common in KRTs. However, we have not found that sarcopenia had a negative impact on clinical health after kidney transplantation. Large study cohorts and multicentre longitudinal studies in the future are urgently needed to explore the prevalence and prognosis of sarcopenia in kidney transplant patients.
Journal Article
Target delivery of a PD-1-TREM2 scFv by CAR-T cells enhances anti-tumor efficacy in colorectal cancer
Background
Chimeric antigen receptor (CAR) -T cell therapy is an efficient therapeutic strategy for specific hematologic malignancies. However, positive outcomes of this novel therapy in treating solid tumors are curtailed by the immunosuppressive tumor microenvironment (TME), wherein signaling of the checkpoint programmed death-1 (PD-1)/PD-L1 directly inhibits T-cell responses. Although checkpoint-targeted immunotherapy succeeds in increasing the number of T cells produced to control tumor growth, the desired effect is mitigated by the action of myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) in the TME. Previous studies have confirmed that targeting triggering-receptor-expressed on myeloid cells 2 (TREM2) on TAMs and MDSCs enhances the outcomes of anti-PD-1 immunotherapy.
Methods
We constructed carcinoembryonic antigen (CEA)-specific CAR-T cells for colorectal cancer (CRC)-specific antigens with an autocrine PD-1-TREM2 single-chain variable fragment (scFv) to target the PD-1/PD-L1 pathway, MDSCs and TAMs.
Results
We found that the PD-1-TREM2-targeting scFv inhibited the activation of the PD-1/PD-L1 pathway. In addition, these secreted scFvs blocked the binding of ligands to TREM2 receptors present on MDSCs and TAMs, reduced the proportion of MDSCs and TAMs, and enhanced T-cell effector function, thereby mitigating immune resistance in the TME. PD-1-TREM2 scFv-secreting CAR-T cells resulted in highly effective elimination of tumors compared to that achieved with PD-1 scFv-secreting CAR-T therapy in a subcutaneous CRC mouse model. Moreover, the PD-1-TREM2 scFv secreted by CAR-T cells remained localized within tumors and exhibited an extended half-life.
Conclusions
Together, these results indicate that PD-1-TREM2 scFv-secreting CAR-T cells have strong potential as an effective therapy for CRC.
Journal Article
Modeling and Analysis of Mixed Traffic Flow Considering Driver Stochasticity and CAV Connectivity Uncertainty
As connected and autonomous vehicle (CAV) technologies are rapidly integrated into modern transportation systems, understanding the dynamics of mixed traffic flow involving both human-driven vehicles (HVs) and CAVs is becoming increasingly important, particularly under uncertain conditions. In this paper, we propose a car-following model framework to investigate the combined effects of driver stochasticity and connectivity uncertainties of CAVs on mixed traffic flow. The proposed framework can capture the inherent stochastic variations in human driving behavior by extending the classic intelligent driver model (IDM) with a Langevin-type stochastic differential equation. A car-following model with multi-anticipation control is developed for CAVs, explicitly incorporating sensor noise, communication delays, and dynamic connectivity. Extensive numerical simulations demonstrate that higher CAV penetration leads to more stable traffic flows. Even with certain levels of connectivity uncertainty, CAVs can still effectively stabilize the traffic. However, driver stochasticity has a pronounced impact on traffic stability—greater variability in driver behavior tends to reduce overall stability. Furthermore, sensitivity analyses reveal that in pure CAV environments, sensor noise, communication delays and communication ranges can affect traffic stability and energy consumption. In contrast, in mixed traffic conditions, the inherent instability of HV behavior tends to dominate and diminish the relative influence of CAV connectivity-related uncertainties. These findings underscore the necessity of robust sensor fusion and error compensation strategies to fully realize the potential of CAV technology. In mixed traffic environments, measures should be taken to minimize the adverse effects of HVs on CAV performance.
Journal Article
Global trends of interstitial lung diseases from 1990 to 2019: an age–period–cohort study based on the Global Burden of Disease study 2019, and projections until 2030
Interstitial lung diseases (ILDs) are indispensable components of chronic respiratory diseases and global health challenges. We aimed to explore the global long-term changes in the prevalence, mortality, and disability-adjusted life years (DALYs) of ILDs; investigate the independent effect of age, period, and cohort; and project the disease burden over the next decade.
Data were retrieved from the Global Burden of Disease (GBD) database 2019. The joinpoint regression model was used to calculate the average annual percent change (AAPC). An age-period-cohort (APC) analysis was employed to measure the independent effect of age, period, and cohort. The Bayesian age-period-cohort (BAPC) model was used to project the global epidemiological trends until 2030.
From 1990 to 2019, the age-standardized prevalence rate (ASPR), age-standardized mortality rate (ASMR), and age-standardized disability-adjusted life years (DALYs) rate (ASDR) of interstitial lung disease and pulmonary sarcoidosis (ILD) slightly increased from 52.66 per 100,000 [95% uncertainty interval (UI) 44.49 to 61.07] to 57.62 per 100,000 (95% UI 49.42 to 65.67), from 1.76 per 100,000 (95% UI 1.41 to 2.22) to 2.17 per 100,000 (95% UI 1.5 to 2.62), and from 41.57 per 100,000 (95% UI 33.93 to 51.92) to 46.45 per 100,000 (95% UI 35.12 to 54.98), whereas the ASPR, ASMR, and ASDR of pneumoconiosis decreased. High social-demographic index (SDI) regions possessed the highest ASPR, whereas low-middle SDI regions had the highest ASMR and ASDR, followed by low-SDI regions in ILD. Middle-SDI regions reported the highest ASPR, ASMR, and ASDR in pneumoconiosis. The age effect showed that the rate ratio (RR) was high in older adults. Period effect indicated that the RR of prevalence increased over time, whereas the RR of mortality and DALYs decreased in men but increased in women. The cohort effect exhibited that the more recent birth cohort had a higher RR than the previous cohort in prevalence. We projected that ASPR, ASMR, and ASDR would stabilize with little variation over the next decade.
The global burden of ILDs remains relatively severe, especially among older adults, in low- and middle-SDI regions. Effective measurements are expected to improve this situation.
Journal Article
A deep learning runoff prediction model based on wavelet decomposition and dynamic feature fusion
To address the stochasticity, time-varying characteristics, and nonlinear dynamics of runoff series, this research proposes a novel deep learning architecture, BWDformer, based on wavelet decomposition and dynamic feature fusion, to enhance the precision of streamflow forecasting. Based on Informer, the model innovatively integrates wavelet decomposition, a dynamic feature fusion module (DFF), and Bayesian optimization to address the limitations of conventional deep learning models in multi-scale feature integration, long-term dependency capture, and dynamic feature adjustment. Specifically, wavelet decomposition is first applied to extract multi-scale features through adaptive time windows, accurately capturing short-term fluctuations, seasonal variations, and long-term trends in runoff data. Then, based on DFF, the feature weights are dynamically adjusted using the attention mechanism to optimize the feature combination, thereby enhancing the model’s ability to analyze complex runoff sequences. Finally, Bayesian optimization is used to efficiently search for hyperparameters, significantly improving the model’s training efficiency. To verify the model’s effectiveness, this study tested it at four hydrological stations: Hongshanhe, Manwan, Baihe, and Tangnaihai. The results show that BWDformer significantly outperforms benchmark models, such as CNN, LSTM, Transformer, and Informer, in terms of MAE, RMSE, R, NSE, and KGE. For example, in Hongshanhe, the MAE is 0.1921, a decrease of 18.82% compared to CNN (0.2366), 4.65% compared to LSTM (0.2014), 15.63% compared to Transformer (0.2277), and 7.87% compared to Informer (0.2086). RMSE decreased by 25.46% compared to CNN, KGE was 0.9651, increased by 18.51% compared to Transformer (0.8143), and increased by 13.43% compared to Informer (0.8506). In Baihe, the MAE is 228.6971 m ³/s, a decrease of about 2.35% compared to CNN (243.0662), and the R value reaches 0.9998, an increase of 4.26% compared to CNN (0.9591). NSE is 0.9972, an increase of 18.73% compared to Transformer (0.8398), KGE is 0.9934, an increase of 9.79% compared to Informer (0.9048). These results verify the excellent performance of BWDformer in improving prediction accuracy, robustness, and practicality.
Journal Article