Risk factors of immune checkpoint inhibitor-related pneumonitis after neoadjuvant immunochemotherapy for resectable NSCLC.

BACKGROUND: The incidence of checkpoint inhibitor-associated pneumonitis (CIP) in advanced non-small cell lung cancer (NSCLC) has been substantiated through large-scale clinical trials or real-world studies. However, reports on CIP incidence within the context of neoadjuvant immunotherapy for resectable NSCLC remain scarce. This study endeavors to investigate the incidence, risk factors, and outcomes of CIP in patients with resectable NSCLC receiving neoadjuvant immunochemotherapy. METHODS: A retrospective, case-control study was conducted on patients diagnosed with NSCLC stages IIA-IIIB who received neoadjuvant immunochemotherapy between January 2018 and September 2022. Patients were stratified into two groups based on the presence or absence of CIP, facilitating a comparative analysis of clinical characteristics, treatment modalities, physiological indicators, and prognostic outcomes . RESULTS: The study cohort comprised 245 patients, with 11.4% (28/245) experiencing CIP. The median period of CIP onset was 70 (range, 40-221) days. The incidence of severe CIP (grade 3-4) was 3.7% (9/245). Patients with CIP showed a higher all-cause mortality rate of 21.4% (6/28) compared to that of patients without CIP. Those who developed CIP exhibited elevated body mass index (BMI) values (p = 0.028) and increased fibrinogen (FIB) levels (p < 0.001), alongside a significant decrease in both diffusing capacity for carbon monoxide (DLCO)% pred (p = 0.001) and DLCO/VA% pred (p = 0.021) after neoadjuvant therapy compared to pre-indicators. Receiver operating characteristic curve (ROC) analysis showed that the area under the ROC curve of three assessed variables (FIB levels, BMI, DLCO) reached 0.806 in predicting CIP occurrence at an early stage. CONCLUSIONS: This cohort demonstrated that elevated BMI, increased FIB levels, and decreased pulmonary diffusion function after neoadjuvant therapy are risk factors of CIP occurrence. Early assessment and continuous monitoring of these indicators are imperative for the predictive identification of CIP, enhancing patient management and outcomes.

Integrated analysis reveals critical cisplatin-resistance regulators E2F7 contributed to tumor progression and metastasis in lung adenocarcinoma.

BACKGROUND: Drug resistance poses a significant challenge in cancer treatment, particularly as a leading cause of therapy failure. Cisplatin, the primary drug for lung adenocarcinoma (LUAD) chemotherapy, shows effective treatment outcomes. However, the development of resistance against cisplatin is a major obstacle. Therefore, identifying genes resistant to cisplatin and adopting personalized treatment could significantly improve patient outcomes. METHODS: By examining transcriptome data of cisplatin-resistant LUAD cells from the GEO database, 181 genes associated with cisplatin resistance were identified. Using univariate regression analysis, random forest and multivariate regression analyses, two prognostic genes, E2F7 and FAM83A, were identified. This study developed a prognostic model utilizing E2F7 and FAM83A as key indicators. The Cell Counting Kit 8 assay, Transwell assay, and flow cytometry were used to detect the effects of E2F7 on the proliferation, migration, invasiveness and apoptosis of A549/PC9 cells. Western blotting was used to determine the effect of E2F7 on AKT/mTOR signaling pathway. RESULTS: This study has pinpointed two crucial genes associated with cisplatin resistance, E2F7 and FAM83A, and developed a comprehensive model to assist in the diagnosis, prognosis, and evaluation of relapse risk in LUAD. Analysis revealed that patients at higher risk, according to these genetic markers, had elevated levels of immune checkpoints (PD-L1 and PD-L2). The prognostic and diagnosis values of E2F7 and FAM83A were further confirmed in clinical data. Furthermore, inhibiting E2F7 in lung cancer cells markedly reduced their proliferation, migration, invasion, and increased apoptosis. In vivo experiments corroborated these findings, showing reduced tumor growth and lung metastasis upon E2F7 suppression in lung cancer models. CONCLUSION: Our study affirms the prognostic value of a model based on two DEGs, offering a reliable method for predicting the success of tumor immunotherapy in patients with LUAD. The diagnostic and predictive model based on these genes demonstrates excellent performance. In vitro, reducing E2F7 levels shows antitumor effects by blocking LUAD growth and progression. Further investigation into the molecular mechanisms has highlighted E2F7's effect on the AKT/mTOR signaling pathway, underscoring its therapeutic potential. In the era of personalized medicine, this DEG-based model promises to guide clinical practice.

Integrase Interactor 1 (INI1) Deficiency in a Lung Cancer Patient Presents Nonresponse to Immunotherapy and Tazemetostat: A Case Report.

Integrase interactor 1 (INI1)-deficient lung cancer is extremely rare, often with poor prognosis, and lacks effective treatment. Previous studies have reported the efficacy of immunotherapy and enhancer of the zeste homolog 2 (EZH2) inhibitor tazemetostat in various types of INI1-deficient tumors, such as sarcomas. However, the effectiveness of these treatments in INI1-deficient lung cancer has not yet been verified. We hereby report a case of a patient who was diagnosed with advanced squamous lung cancer with INI1 deficiency and received chemotherapy, immunotherapy, and tazemetostat treatments successively. The patient showed optimal response in the initial chemotherapy combined with anti-programmed cell death protein 1 (PD-1) immunotherapy, made rapid progress in the subsequent stage of maintenance immunotherapy, and showed nonresponse to tazemetostat. To the best of our knowledge, this is the first case of a lung cancer patient with INI1 deficiency who received tazemetostat treatment.

Targeting immune checkpoints on tumor-associated macrophages in tumor immunotherapy.

Unprecedented breakthroughs have been made in cancer immunotherapy in recent years. Particularly immune checkpoint inhibitors have fostered hope for patients with cancer. However, immunotherapy still exhibits certain limitations, such as a low response rate, limited efficacy in certain populations, and adverse events in certain tumors. Therefore, exploring strategies that can improve clinical response rates in patients is crucial. Tumor-associated macrophages (TAMs) are the predominant immune cells that infiltrate the tumor microenvironment and express a variety of immune checkpoints that impact immune functions. Mounting evidence indicates that immune checkpoints in TAMs are closely associated with the prognosis of patients with tumors receiving immunotherapy. This review centers on the regulatory mechanisms governing immune checkpoint expression in macrophages and strategies aimed at improving immune checkpoint therapies. Our review provides insights into potential therapeutic targets to improve the efficacy of immune checkpoint blockade and key clues to developing novel tumor immunotherapies.

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction.

Accurate hyperspectral remote sensing information is essential for feature identification and detection. Nevertheless, the hyperspectral imaging mechanism poses challenges in balancing the trade-off between spatial and spectral resolution. Hardware improvements are cost-intensive and depend on strict environmental conditions and extra equipment. Recent spectral imaging methods have attempted to directly reconstruct hyperspectral information from widely available multispectral images. However, fixed mapping approaches used in previous spectral reconstruction models limit their reconstruction quality and generalizability, especially dealing with missing or contaminated bands. Moreover, data-hungry issues plague increasingly complex data-driven spectral reconstruction methods. This paper proposes SpectralMAE, a novel spectral reconstruction model that can take arbitrary combinations of bands as input and improve the utilization of data sources. In contrast to previous spectral reconstruction techniques, SpectralMAE explores the application of a self-supervised learning paradigm and proposes a masked autoencoder architecture for spectral dimensions. To further enhance the performance for specific sensor inputs, we propose a training strategy by combining random masking pre-training and fixed masking fine-tuning. Empirical evaluations on five remote sensing datasets demonstrate that SpectralMAE outperforms state-of-the-art methods in both qualitative and quantitative metrics.

Analysis of healthcare needs differences and influencing factors among elderly population: Evidence from Yangtze River Delta region, China.

The quality of healthcare services is related to the quality of life of older people in their later years. A comprehensive evaluation of the healthcare needs of the elderly is the basic basis for providing targeted healthcare services for the elderly population. Taking the Yangtze River Delta region of China as an example, this article constructs an index system for evaluating the healthcare needs among the elderly, and explores the healthcare needs of the elderly and the influencing factors based on Dataset of the Fourth Sample Survey on the Living Conditions of China's Urban and Rural Older Persons. The study concludes that: Age 75 is the cut-off point for the healthcare needs of the elderly, with the growth of healthcare needs of the elderly aged 60-75 relatively flat and the growth of healthcare needs of the elderly aged 75 and above rising sharply. There is a wide variation in the Daily Activities Care Index, Incontinence Index and Aids Use Index scores and their indicators, as reflected in the differences in healthcare needs of older people in different age groups and in urban and rural areas. Healthcare needs of the elderly show a high positive correlation with the Daily Activities Care Index and Incontinence Index; healthcare needs of the elderly show a cubic function curve correlation with the assistive device use index. Community elderly healthcare services are conditions that influence the demand for elderly healthcare, but the low proportion of elderly people staying in elderly care institutions is due to a combination of low affordability, general quality of elderly care services and cultural factors. Educational attainment and marital status are micro-conditions that influence the demand for healthcare in old age. In terms of educational attainment, elderly people who have not attended school have an increasing need for healthcare as they get elderly. In terms of marriage, there is a strong negative correlation between the willingness to healthcare needs among elderly people with a spouse and a strong willingness to healthcare needs among elderly people who are widowed.

An Adaptive Fusion Attitude and Heading Measurement Method of MEMS/GNSS Based on Covariance Matching.

Aimed at the problem of filter divergence caused by unknown noise statistical characteristics or variable noise characteristics in an MEMS/GNSS integrated navigation system in a dynamic environment, on the basis of revealing the parameter adjustment logic of covariance matching adaptive technology, a fusion adaptive filtering scheme combining innovation-based adaptive estimation (IAE) and the adaptive fading Kalman filter (AFKF) is proposed. By setting two system tuning parameters, for the process noise covariance adaptation loop and the measurement noise covariance adaptation loop, covariance matching is sped up and achieves an effective suppression of filter divergence. The vehicle-mounted experimental results show that the mean square error of the combined attitude error obtained based on the fusion filtering method proposed in this paper is better than 0.5°, and the mean square error of the heading error is better than 1.5°. The results can provide technical support for the continuous extraction of low-cost attitude information from mobile platforms.

Predicting Social Events with Multimodal Fusion of Spatial and Temporal Dynamic Graph Representations.

Big data has been satisfactorily used to solve social issues in several parts of the word. Social event prediction is related to social stability and sustainable development. However, current research rarely takes into account the dynamic connections between event actors and learning robust feature representations of social events. Inspired by the graph neural network, we propose a novel Siamese Spatial and Temporal Dynamic Network for predicting social events. Specifically, we use multimodal data containing news articles and global events to construct dynamic graphs based on word co-occurrences and interactions between event actors. Dynamic graphs can model the evolution of social events. By employing the fusion of spatial and temporal dynamic graph representations from heterogeneous historical data, our proposed model predicts the occurrence of future social events for the target country. Qualitative and quantitative analysis of experiment results on multiple real-word datasets shows that our proposed method is competitive against several approaches for social event prediction.

Visual Relationship Detection: A Survey.

Visual relationship detection (VRD) is one newly developed computer vision task, aiming to recognize relations or interactions between objects in an image. It is a further learning task after object recognition, and is important for fully understanding images even the visual world. It has numerous applications, such as image retrieval, machine vision in robotics, visual question answer (VQA), and visual reasoning. However, this problem is difficult since relationships are not definite, and the number of possible relations is much larger than objects. So the complete annotation for visual relationships is much more difficult, making this task hard to learn. Many approaches have been proposed to tackle this problem especially with the development of deep neural networks in recent years. In this survey, we first introduce the background of visual relations. Then, we present categorization and frameworks of deep learning models for visual relationship detection. The high-level applications, benchmark datasets, as well as empirical analysis are also introduced for comprehensive understanding of this task.

A Hybrid Seasonal Autoregressive Integrated Moving Average and Denoising Autoencoder Model for Atmospheric Temperature Profile Prediction.

Atmospheric parameter profile plays a vital role in the studies on meteorology and quantitative remote sensing. Besides measurement of the radiosonde and satellite remote sensing, the atmospheric temperature profile can be predicted by the time series model. However, the performance of the time series model is limited by the weak correlation of temperature data in the stratosphere. In this study, to predict monthly mean atmospheric temperature profile, a hybrid model named seasonal autoregressive integrated moving average and denoising autoencoder (SARIMA-DAE), which combines the traditional time series model with an artificial neural network (ANN), is proposed. The SARIMA is first used to predict the temperature at each altitude level independently; then, the DAE is employed to denoise the predicted temperature profile by SARIMA. The data set used is the L3 monthly mean gridded product of atmospheric infrared sounder, an instrument aboard NASA's Aqua satellite. Taking Maoming, a city in China, as an example, the absolute errors are within 1 K. The mean squared error and mean absolute percentage error on the test set are 0.12 and 0.0012, respectively. Compared with SARIMA, support vector machine regression, and ANN models, experimental results show that the SARIMA-DAE model improves the prediction accuracy at almost all altitude levels. Especially in the stratosphere, the advantage of the hybrid model is more pronounced. The model we proposed should, therefore, be of value to estimate missing data and predict atmospheric parameter profiles.

Design, fabrication, and measurement of an anisotropic holographic metasurface for generating vortex beams carrying orbital angular momentum: erratum.

This erratum makes corrections to Eqs. (7) and (9) of Opt. Lett.44, 1452 (2019).OPLEDP0146-959210.1364/OL.44.001452.

Design, fabrication, and measurement of an anisotropic holographic metasurface for generating vortex beams carrying orbital angular momentum.

In this Letter, to the best of our knowledge, a novel anisotropic surface impedance holographic metasurface antenna for generating orbital angular momentum (OAM) is developed through design, fabrication, and measurement at radio frequencies. The classical leaky-wave theory and a microwave holography method are combined to realize vortex waves carrying different OAM modes flexibly. The holographic metasurface composed of subwavelength quasi-periodic anisotropic metallic patches on a grounded dielectric substrate operates by exciting the interference patterns. The interferences are generated between a reference wave excited by the monopole antenna and a field with the desirably shaped wavefront-carrying helical phase. Numerical simulation has shown good agreement with the experimental results, which lays a solid foundation for holographic metasurface antennas having the potential for OAM generation at radio frequencies.

Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA.

An efficient parallel computation using graphics processing units (GPUs) is developed for studying the electromagnetic (EM) backscattering characteristics from a large three-dimensional sea surface. A slope-deterministic composite scattering model (SDCSM), which combines the quasi-specular scattering of Kirchhoff Approximation (KA) and Bragg scattering of the two-scale model (TSM), is utilized to calculate the normalized radar cross section (NRCS in dB) of the sea surface. However, with the improvement of the radar resolution, there will be millions of triangular facets on the large sea surface which make the computation of NRCS time-consuming and inefficient. In this paper, the feasibility of using NVIDIA Tesla K80 GPU with four compute unified device architecture (CUDA) optimization strategies to improve the calculation efficiency of EM backscattering from a large sea surface is verified. The whole GPU-accelerated SDCSM calculation takes full advantage of coalesced memory access, constant memory, fast math compiler options, and asynchronous data transfer. The impact of block size and the number of registers per thread is analyzed to further improve the computation speed. A significant speedup of 748.26x can be obtained utilizing a single GPU for the GPU-based SDCSM implemented compared with the CPU-based counterpart performing on the Intel(R) Core(TM) i5-3450.

Dual-polarized reflectarray for generating dual beams with two different orbital angular momentum modes based on independent feeds in C- and X-bands.

In this paper, a dual-band dual-polarized reflectarray for generating dual beams with respect to carrying two different orbital angular momentum (OAM) topological charges operating in the C-band in horizontal polarization and in the X-band in vertical polarization is proposed, with two separate horns performing on the two proposed bands as the feeding. The proposed reflectarray consists of two band reflective element cells operating in two orthogonal directions. Owing to the two composing elements orthogonally interleaved on the reflectarray surface, the corresponding phase compensation performance in one band can be slightly affected by the elemental resonance in another band; thus, the degree of the coupling between the elements with different-band operations can be neglected, resulting in fairly independent phase compensation. In other words, the desired OAM generation reflectarray, to some extent, can be developed based on two different frequency band OAM reflectarrays at the same aperture. In addition, simulations and measurements strongly suggest the feasibility and the validity of the approach, which provides a solid foundation for the application of multi-band reflectarrays to the multi-OAM-mode generation.

Features of X-Band Radar Backscattering Simulation Based on the Ocean Environmental Parameters in China Offshore Seas.

The X-band marine radar has been employed as a remote sensing tool for sea state monitoring. However, there are few literatures about sea spectra considering both the wave parameters and short wind-wave spectra in China Offshore Seas, which are of theoretical and practical significance. Based on the wave parameters acquired from the European Centre for Medium-Range Weather Forecasts reanalysis data (ERA-Interim reanalysis data) during 36 months from 2015 to 2017, a finite depth sea spectrum considering both wind speeds and ocean environmental parameters is established in this study. The wave spectrum is then built into a modified two-scale model, which can be related to the ocean environmental parameters (wind speeds and wave parameters). The final results are the mean backscattering coefficients over the variety of sea states at a given wind speed. As the model predicts, the monthly maximum backscattering coefficients in different seas change slowly (within 4 dB). In addition, the differences of the backscattering coefficients in different seas are quite small during azimuthal angles of 0° to 90° and 270° to 360° with a relative error within 1.5 dB at low wind speed (5 m/s) and 2 dB at high wind speed (10 m/s). With the method in the paper, a corrected result from the experiment can be achieved based on the relative error analysis in different conditions.

Efficient continuous-wave and short-pulse Ho3+-doped fluorozirconate glass all-fiber lasers operating in the visible spectral range.

In this work, efficient visible Ho3+-doped fluorozirconate glass (Ho:ZBLAN) all-fiber lasers operating in continuous-wave (CW) and Q-switching regimes are experimentally demonstrated. The combination of a direct blue pump, a highly-doped Ho:ZBLAN fiber and the fiber end-facet mirrors contributes to a simple all-fiber configuration. A tunable laser emission in the green spectral range of 543-550 nm is achieved with >150 mW output power and a tunable deep-red laser around 754-758 nm is also obtained with about 16 mW output power. Interestingly, stable visible self-Q-switched operation was successfully observed. For the green Q-switched all-fiber laser, a maximum single pulse energy of 196 nJ is realized with a repetition rate of 97.66 kHz and a pulse duration of 605 ns. As the pump power is increased, the deep-red Q-switched all-fiber laser has the pulse repetition rate from 59.88 to 100.5 kHz and the pulse duration from 4.85 to 2.02 µs, corresponding to the maximum pulse energy of 58 nJ. To the best of our knowledge, this work is the first demonstration of Ho:ZBLAN all-fiber lasers emitting in the visible spectral range (i.e., both green and deep-red wavelengths).

Investigation on the effect of output mirror transmission in WS2-based red-light passively Q-switched Pr:ZBLAN all-fiber lasers.

We report the experimental investigation of visible passively Q-switched Pr3+-doped all-fiber lasers with tungsten disulfide (WS2) saturable absorber, where red-light short-pulse generations from different output mirror transmissions are systemically characterized. The proposed simple and compact all-fiber linear cavity was constructed by a fiber-pigtail-based blue laser-diode pump, a Pr3+-doped fluorozirconate glass active fiber, and the fiber end-facet mirrors. Integrating a free-standing layered WS2 film into the laser cavity initiated the Q-switching operation. Stable microsecond-duration output pulses with kilohertz repetition rates are achieved, corresponding to a few mW/nJ average output power and single-pulse energy. The comparisons on red-light Q-switched output parameters for output transmissions of both ∼40% and ∼80% are performed. This work could provide a useful guideline to manipulate the output performance of visible pulsed all-fiber lasers for various practical applications.

A partition-based active contour model incorporating local information for image segmentation.

Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.