Lactylation, as a post-translational modification, plays a role in tumor proliferation, metabolism, and the remodeling of the tumor microenvironment (TME). Emerging studies have revealed that exosomes regulate lactate metabolism by delivering functional molecules (such as lncRNAs, metabolic enzymes, etc.) thereby driving protein lactylation and establishing a novel intercellular communication mechanism. Furthermore, acidic microenvironments induce the release of immunosuppressive exosomes, amplifying immune evasion. Here, we summarize the current understanding of lactylation and exosome regulation in the TME and their impact on immune evasion. We explore the pivotal role of the "exosome-lactate-lactylation" axis in tumor metabolic reprogramming, metastasis, and immunosuppression, proposing targeted strategies against this axis.

To address the challenges of wave packet broadening and signal distortion caused by multimodality and dispersion effects in Lamb wave-based damage detection for carbon fibre-reinforced polymer (CFRP) laminates, this paper proposes an imaging method that combines multimodal dispersion compensation and compressive sensing (CS). First, the Rayleigh-Lamb equation is solved using the T300/5028 material parameters to establish the dispersion characteristic model. A baseline-free multimodal dispersion compensation technique is developed for performing frequency-domain phase correction and time-domain signal reconstruction, allowing accurate estimation of the compensation distance between each sensor and the damage. Subsequently, a CS-based imaging algorithm that fuses delay-and-sum (DAS) and sparse reconstruction is proposed and verified using finite-element simulations and experimental tests. The results show maximum localisation errors of 2.03 +/- 0.05 mm in simulations and 2.31 +/- 0.56 mm in experiments. This work provides theoretical insights and experimental support for the high-precise identification of delamination damage in anisotropic composite materials.

The Acoustic Influence Map (AIM) shows the acoustic energy distribution of total focusing images at different spatial positions. To address the computational complexity and low efficiency of existing AIMs, a deep-conditional diffusion model is proposed. An improved U-Net is adopted as the backbone network, and a four-level encoder-decoder structure is constructed. The input channels are expanded to 34, and discrete depths are converted into 32-channel feature maps to match the image dimensions by a learnable embedding layer. The mask-depth embedding dual-condition input mechanism is realised by a single-channel mask image and a noise image. The generated images are evaluated using the Structural Similarity Index (SSIM) and Pearson Correlation Coefficient (CC). The range of SSIM value is from 0.86 to 0.93, indicating that the model can effectively preserve the details of the images. The range of CC value is from 0.68 to 0.86, demonstrating that the model is able to reconstruct the geometric structure of total focusing images. The normalised amplitudes predicted by the AIM are compared with the total focusing images of holes. The results show a correlation coefficient of 0.96 between the two datasets, indicating a strong positive correlation between the predicted values of the acoustic influence map and the actual imaging results.

OBJECTIVE:The aim of this study was to evaluate the therapeutic effect of electroacupuncture (EA) in a rat model of stress urinary incontinence (SUI) induced by vaginal distension (VD). The potential mechanisms underlying this process were also explored. METHODS:Virgin Sprague-Dawley rats underwent VD (to model SUI) or a sham operation, followed by EA or no treatment. Cystometry and leak point pressure (LPP) testing were employed to demonstrate the impact of EA on the micturition reflex and urethral closure function. mRNA expression of α1A and α1D adrenoceptors and 5-hydroxytryptamine (5-HT)2C and 5-HT2A receptors were examined in spinal segments using real-time qRT-PCR, Western blotting and immunohistochemistry (IHC). The individual role of 5-HT2A and 5-HT2C receptors were distinguished with selective antagonists (MDL 100907 and SB 242084, respectively). RESULTS:EA treatment successfully reversed the decrease of LPP induced by VD without any significant effect on the micturition reflex in this rat model of SUI. VD did not change bladder basic pressure (BP), voided volume or bladder contraction. Multiple approaches including qRT-PCR, Western blotting and IHC revealed over-expression of 5-HT2C and 5-HT2A receptors but not α1A or α1D adrenoceptors in the L6-S2 spinal cord of these rats. Administration of the 5-HT2C antagonist (SB 242084) largely eliminated EA-mediated mitigation of the decrease in LLP caused by VD, while the 5-HT2A antagonist (MDL 100907) had no effect under these conditions. CONCLUSION:EA improves impaired urethral closure capacity induced by VD in female rats, and it appears that the 5-HT2C receptor plays a critical role in this effect. It is reasonable to speculate that EA represents a promising treatment for SUI caused by childbirth trauma.

The goal of this study was to maintain a high cure rate for nasopharyngeal carcinoma (NPC) while reducing the area of radiation delivered to the target volume to minimize the incidence of mucosal ulcers with a toxicity grade of 3 or above. The primary objective was to reduce the incidence of mucosal ulcers with a toxicity grade of 3 or above post-radiotherapy. Between January 2023 and March 2024, 20 eligible patients received reduced-target radiotherapy, and none withdrew from the study. Acute and late toxicity reactions and survival rates after treatment were evaluated. As of the data cutoff date (January 06, 2026), the median follow-up period was 27 months (range: 21–33 months). All patients were alive with no locoregional recurrence, except for one patient who developed femoral metastasis at 21 months post-radiotherapy. The incidence of mucosal ulcers with a toxicity grade of 3 or above, the primary endpoint of this study, was 0. No patients exhibited radiotherapy-related acute xerostomia, dermatitis, or mucositis with a toxicity grade of 3 or above. From 3 months after the end of radiotherapy to the end of follow-up, no dysphagia, subcutaneous soft tissue injury, or nasopharyngeal necrosis of any grade and no cases of grade 3 or above xerostomia or hearing impairment were observed. In carefully selected patients, this reduced-target radiotherapy strategy is anatomically and dosimetrically feasible, with a favorable short-term safety profile.

Addressing the issue of lifespan prediction for electronic packages under thermal loading, this paper proposes a method for predicting the lifespan of electronic packages based on ultrasonic microimaging. Firstly, experimental samples equipped with flip-chip packages were designed and fabricated and subjected to aging through thermal cycle acceleration tests. Ultrasonic microscopy was utilized to periodically acquire ultrasonic image data for monitoring solder joint degradation. Secondly, the internal ultrasonic wave propagation mechanism within electronic packages was investigated, establishing a qualitative relationship between the intensity in the central region of the solder joint’s ultrasonic image and internal defects within the joint. Image processing techniques were applied to enhance the quality of the solder joint images, and the mean intensity in the central region of the solder joint image was extracted as a failure feature. Finally, based on the extracted failure feature, a data-driven failure model for solder joints was developed, which predicts the lifespan of the solder joints based on cumulative failure probability. The research results indicate that the proposed model accurately describes the failure process of solder joints and effectively differentiates the lifespan variations among solder joints at different locations on the chip. This provides theoretical support for the reliability assessment of electronic package solder joints and holds practical value for enhancing the overall reliability of electronic packaging components.

Immunotherapy has become a promising and transformative approach for treating advanced or treatment-resistant bladder cancer (BCa). However, its efficacy remains limited due to the immunosuppressive tumor microenvironment (TME) and insufficient immune cell infiltration. Photothermal therapy (PTT), which could cause immunogenic cell death (ICD) in tumor tissue, has been explored as a synergistic approach for bladder cancer immunotherapy. Yet, thermal resistance in cancer cells often undermines the effectiveness of PTT. To address these challenges, we proposed a novel strategy that combines PTT with cuproptosis, a recently identified form of ICD, by engineering Tim-3-overexpressing T cell membrane-coated nanoparticles (Tim3@PHSM@IC) to enhance BCa immunotherapy. The overexpression of Tim-3 on the T cell membrane enabled precise targeting of tumor cells and competitively inhibited the Tim-3 receptor on T cells through recognition of Galectin-9. In vitro, Tim3@PHSM@IC nanoparticles effectively induced photothermal cytotoxicity and robust cuproptosis. In vivo, these nanoparticles significantly inhibited tumor growth in multiple BCa mouse models. Flow cytometry (FCM) and RNA sequencing (RNA-seq) analyses revealed that Tim3@PHSM@IC nanoparticles reprogrammed the TME by activating immune-related genes and enhancing ICD This study highlights the potential of Tim3@PHSM@IC nanoparticles in overcoming the immunosuppressive TME and improving the efficacy of BCa immunotherapy by integrating PTT and cuproptosis.

In digital holographic imaging measurement, three-dimensional information, such as shape, height, and refractive index distribution, can be obtained by analyzing the phase information of the object to be measured. However, the misalignment of the measurement optical path, environmental disturbances, and phase differences of the optical components lead to phase distortions that seriously affect the accuracy of the measurement. In order to accurately recover the real phase information, a phase distortion compensation method based on Residual Squeeze-and-Excitation Nested U-Net Architecture (RS-UNet++) is proposed in this paper. This method takes U-Net++ as the basic network architecture, uses the histogram threshold segmentation algorithm to generate the binary image of the phase, and extracts the phase distortion scatter data by the random library selection method. The scatter data are then combined with the phase data generated by random matrix enlargement and Gaussian function superposition to train the network model. Finally, the simulation and experiment are used to verify the effectiveness and accuracy of the proposed method. The results show that compared with the IRLS, Poly-fit, PCA, CNN, and Res-UNet methods, the proposed method achieves the optimal phase distortion compensation effect in terms of standard deviation, global error, and Pearson's correlation coefficient and greatly improves the accuracy of phase recovery.

Compared to traditional tumor treatment modalities, such as radiotherapy, chemotherapy, and surgical intervention, tumor immunotherapy offers potential benefits by enhancing the immune system’s functionality, diminish immune evasion, and establish enduring immune memory, ultimately aiming for the destruction or eradication of tumor cells. However, the intricate tumor microenvironment (TME) poses challenges for immunotherapy due to its suppressive effects on immune cell activity, leading to issues such as low immune responsiveness and insufficient targeting. Sonodynamic immunotherapy (SDT) emerges as an innovative therapeutic approach， where sonosensitizers localize around tumor cells and, upon ultrasound (US) stimulation, generate substantial reactive oxygen species (ROS). This mechanism facilitates targeted tumor cell killing and enhances treatment specificity. ROS also induce immunogenic cell death (ICD) through various pathways, bolstering the immune response. Recent research suggests that piezoelectric biomaterials, used as sonosensitizers, can amplify the anti-tumor effects of SDT. The piezoelectric effect of these materials enhances ROS production efficiency, improves the TME, mitigates immune cell suppression, and promotes ICD. This review article provides an overview of piezoelectric biomaterials classification, introduce the piezoelectric effect, and examines the applications of piezoelectric biomaterials in sonodynamic immunotherapy. It serves as a reference for the development of piezoelectric materials and the advancement of tumor treatment strategies.

In the ultrasonic detection of weld defects, addressing issues such as small-sample multi-class imbalanced distribution of echo signals and lightweight requirements for classification models, a welding defect ultrasonic signal recognition method based on Gramian angular summation field (GASF), improved auxiliary classifier generative adversarial network (ACGAN), and WOA-ShuffleNet V1 is proposed. First, ACGAN is enhanced by integrating a frequency-aware module, latent space optimisation, and optimised loss functions to improve generated sample quality. Then, through comparative analysis of expansion ratios and the impact of generative models on classification results, the effectiveness of the improved ACGAN in augmenting multi-class imbalanced small-sample data is validated. Finally, the whale optimisation algorithm (WOA) is employed to optimise hyperparameters of ShuffleNet V1. Experimental results show that on the expanded data, the method maintains 90.75% recognition accuracy while reducing FLOPs, model size and number of parameters, thus achieving a balance between lightweighting and classification performance.

Phase unwrapping, a critical step in obtaining holographic information, plays a significant role in the field of digital holography, particularly in applications such as fringe projection for 3D imaging, synthetic aperture radar, and magnetic resonance imaging. Traditional phase unwrapping algorithms often suffer from error accumulation, high computational costs, and poor performance in low signal-to-noise ratio (SNR) environments. To address these issues, this paper proposes a novel deep learning framework, named as Self-Attention Dense Residual Network (SA-DRNet), for phase unwrapping. To obtain continuous phase, we initially employed a dense network for multiple extractions of phase features. However, to alleviate the phase discontinuities and phase jumps caused by gradient issues, we integrated residual connections within the dense network. Finally, we incorporated a self-attention module to enhance the global phase information restoration, including the background phase, thereby achieving high-precision phase acquisition. Additionally, we established an off-axis digital holographic optical system to capture the holograms of the USAF resolution test target and artificial ink dots. Finally, the robustness of the proposed algorithm under severe noise conditions was first verified through numerical simulations, followed by experimental validation of its effectiveness.

The accurate acquisition of the phase information is crucial for the three-dimensional shape reconstruction of an object. However, the inverse tangent operation in the phase extraction process will inevitably cause the phase wrapping phenomenon, resulting in phase discontinuity. Therefore, a digital holographic phase unwrapping method based on Deeplabv3plus-Inverted-Residual-Attention (Dv3p-IRA) is proposed in this paper. This method takes Deeplabv3+ as the basic framework and adopts the encoder-decoder structure: the encoder achieves multi-scale feature extraction through dense block and convolutional block attention module-atrous spatial pyramid pooling (CBAM-ASPP), while the decoder achieves phase reconstruction through cross-layer feature fusion and up-sampling. Random matrix enlargement (RME) and Gaussian function superposition (GFS) methods are used to construct the dataset, cross-entropy loss and Dice loss are integrated as the loss function to optimize the network parameters, and a reflective off-axis digital holographic optical path system is constructed for experimental verification. The simulation and experimental results show that compared with other methods, the Dv3p-IRA achieves higher accuracy, with smaller fluctuations in the error value range and more stable model performance. In addition, it can effectively realize the separation of object information and background, and the reconstructed phase shape has good smoothness and continuity. Therefore, the proposed method not only realizes high-precision phase recovery, but also effectively deals with the holograms with speckle noise, and shows significant advantages in phase region segmentation and noise robustness.

Immunotherapy is a highly promising cancer treatment method. However, it is limited by low immunogenicity and an immunosuppressive microenvironment, which could be relieved by immunogenic cell death (ICD). Currently, effective ICD is primarily achieved through apoptosis induction, but tumor cells' resistance to apoptosis limits its antitumor efficacy. Therefore, developing new cell death modalities with high immunogenicity for cancer immunotherapy is of great significance. Cuproptosis, a newly discovered form of programmed cell death, can effectively circumvent tumor cells' resistance to apoptosis. Various Cu ionophores have been studied as anticancer drugs to promote cuproptosis, but the lack of tumor specificity remains one of the major challenges in this field. In contrast, nanoparticles tend to preferentially accumulate in tumor tissues due to the enhanced permeability and retention (EPR) effect, and they can be surface-modified to achieve active tumor targeting capabilities. Recently, many unique physicochemical properties of nanoparticles have been designed as nano-inducers of cuproptosis, successfully enhancing immunotherapy. Based on this, this review detailedly summarized various strategies and applications of nanoparticles-induced cuproptosis in tumor cells. The role of Cu metabolism and homeostasis in tumorigenesis and development, the molecular mechanisms of cuproptosis and different cuproptosis inducers with promising application prospects, as well as the interaction between cuproptosis and immunotherapy have also been reviewed. Finally, we presented the limitations and future prospects of cuproptosis nano-inducers, hoping to provide a new strategy to enhance antitumor immunotherapy.

In digital holographic measurement, when light waves pass through inhomogeneous media or surfaces, speckle noise is generated, resulting in random, granular light and dark spots in the hologram, which greatly reduces the image quality. Therefore, in order to improve the image quality of holographic reconstruction, a noise reduction method based on the BM3D improved convolutional neural network (CNN) is proposed in this paper. Firstly, the similarity and important statistical information between blocks can be obtained by using BM3D. Then, the denoising convolutional neural network (DnCNN) is used to learn the relationship between the noise of a large number of samples and the noise image, and further purify the image to retain the details for a better denoising effect. Finally, a reflective off-axis digital holographic optical path system is constructed to collect the holograms of the test samples, and the reconstructed images are obtained by the Fresnel diffraction method to constitute a dataset with the simulated holographic reconstructed images to validate the proposed method in this paper, compared to the other methods, such as DnCNN, convolutional blind denoising network (CBDNet), BM3D, and Wiener filtering. The experimental results of qualitative and quantitative analyses show that the proposed method combines the advantages of traditional algorithms and deep learning, significantly enhances the robustness of the system, optimizes the denoising performance, and preserves the details of the reconstructed image to the greatest extent. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

Immune checkpoint blockers (ICBs) have been applied for cancer therapy and achieved great success in the field of cancer immunotherapy. Nevertheless, the broad application of ICBs is limited by the low response rate. To address this issue, increasing studies have found that the induction of immunogenic cell death (ICD) in tumor cells is becoming an emerging therapeutic strategy in cancer treatment, not only straightly killing tumor cells but also enhancing dying cells immunogenicity and activating antitumor immunity. ICD is a generic term representing different cell death modes containing ferroptosis, pyroptosis, autophagy and apoptosis. Traditional chemotherapeutic agents usually inhibit tumor growth based on the apoptotic ICD, but most tumor cells are resistant to the apoptosis. Thus, the induction of non-apoptotic ICD is considered to be a more efficient approach for cancer therapy. In addition, due to the ineffective localization of ICD inducers, various types of nanomaterials have been being developed to achieve targeted delivery of therapeutic agents and improved immunotherapeutic efficiency. In this review, we briefly outline molecular mechanisms of ferroptosis, pyroptosis and autophagy, as well as their reciprocal interactions with antitumor immunity, and then summarize the current progress of ICD-induced nanoparticles based on different strategies and illustrate their applications in the cancer therapy.

The Total Focusing Method (TFM) focuses pixels using the Delay and Sum (DAS) beamforming technique, which relies solely on the temporal information of the full matrix capturing dataset while ignoring its spatial information, and the image resolution and contrast achievable with TFM are limited. In this work, a parallel sparse delay multiply and sum (PSDMAS) focusing imaging algorithm based on sparse arrays and parallel computing is proposed to improve contrast resolution and imaging efficiency. A sparse array optimisation method is applied to reduce the amount of data. A ratio of main-lobe width and side-lobe peak was constructed as the fitness function and a genetic algorithm was used to find the optimal solution for the array arrangement. Delay Multiply and Sum (DMAS) was employed to enhance the spatial coherence and suppress the clutter artefacts. Parallel computing strategies were implemented to improve imaging efficiency. To validate the effectiveness of the algorithm, we processed the full matrix data collected from simulations and experiments using PSDMAS, the imaging results of the PSDMAS provided a considerable improvement in Array Performance Indicator (API), and better lateral spatial resolution was also achieved. The computation time of PSDMAS was reduced by 99.9% compared to conventional DMAS.

In digital holographic measurement, the hologram phase is extracted using an inverse tangent function, resulting in a wrapped phase that is constrained to be (-pi, pi, pi ]. However, the phase contains the height information of the object, which is crucial for accurate measurement of the object contour. Therefore, a digital holographic phase unwrapping method based on SRDU-Net (Separable-Residual-Dense-Inverted U-Net) is proposed in this paper. The SRDU-Net network is constructed by introducing depth-separable convolution, inverted residuals and dense blocks with U-Net as the network framework to achieve high-precision hologram phase recovery. This network adopts depth-separable convolution instead of traditional convolution, combines the inverse residual connection to construct a lightweight convolution structure, and defines dense blocks with this structure to form a lightweight grouped deep convolution network. Meanwhile, the Leaky ReLU activation function is used to introduce the learning rate mechanism to optimize the network parameters with Huber and MSE (mean square error) as the combined loss function. The simulated phase dataset is used to train the network, the trained network model is tested for speckle noise immunity, and phase unwrapping experiments are performed on the collected holograms of the test samples. The results show that SRDU-Net improves the SSIM by 0.1% and reduces the RMSE by 86% over Res-UNet (Residual-UNet). Therefore, the proposed method can realize high-precision recovery of digital holographic wrapped phases, and has a good robustness to phase unwrapping of holograms containing a high degree of speckle noise.

Modern 3D microelectronic packages frequently exhibit a significant overlap of reflected ultrasonic echoes, often exceeding 50 % due to the diminishing thickness of internal structural layers. This overlap results in a marked degradation of ultrasonic image quality during C-scanning. To tackle this issue, a novel multiresolution sparse signal representation algorithm is proposed to achieve multiresolution decomposition of overlapping ultrasonic signals. The algorithm begins with SMP algorithm using a standard Gabor dictionary for initial decomposition. In each subsequent iteration, the dictionary is refined by narrowing the dictionary parameter boundaries while dividing the signal into shorter segments. Through this iterative refinement, the decomposition achieves increased precision without necessitating the enlargement of the dictionary size. Our approach not only ensures a more precise decomposition but also enhances the alignment of dictionary atoms with ultrasonic echoes, especially in instances of echo overlap. The efficacy of this algorithm in accurately separating and estimating ultrasonic echoes has been validated through both simulated and experimental ultrasonic signals. This study strengthens Sparse Signal Representation (SSR) in ultrasonic Non-Destructive Evaluation (NDE) by addressing the challenge of unstable decomposition when the dictionary size surpasses a certain threshold, thereby enhancing the reliability of SSR in the failure analysis of 3D microelectronic packaging.

The miniaturization, ultra-thin and multi-layer complex structure of microelectronic packaging complicates the coupling acoustic field of ultrasonic waves and internal defects in the packaging, making accurate defect detection very difficult. In this paper, the finite element models of flip chip (FC) packaging and ball grid array (BGA) packaging are established to investigate the coupling acoustic field characteristics of ultrasonic waves and defects. In addition, based on the ultrasonic pitch and catch technique, the coupling laws of ultrasonic waves of different frequencies and the defects of different types, positions and sizes are analyzed by simulation, and the relationship between the relative amplitudes of the bottom waves and the sizes of different defects is revealed. Two specimens of microelectronic packaging are designed and fabricated to carry out the experimental studies using an ultrasonic signal acquisition system. The simulation and experimental results show that the relationship between the defects with small changes in the same location and the relative amplitudes of the bottom waves is basically linear, while the relationship between the solder ball extension defects with large changes and the relative amplitudes of the bottom waves is basically logarithmic, which provides a theoretical guidance for accurate evaluation of the type, size and location of defects in the practical detection.

Despite advances in treatment, prostate cancer remains a leading cause of cancer-related deaths among men, highlighting the urgent need for innovative therapeutic strategies. MicroRNAs (miRNAs) have emerged as key regulatory molecules in cancer biology. In this research, we investigated the tumor-suppressive role of miR-5100 in PCa and its underlying molecular mechanism. By using RT-qPCR, we observed lower miR-5100 expression in PCa cell lines than in benign prostate cells. Functional assays demonstrated that miR-5100 overexpression significantly suppressed PCa cell proliferation, migration, and invasion. By using RNA-sequencing, we identified 446 down-regulated and 806 upregulated candidate miR-5100 target genes overrepresenting cell cycle terms. Mechanistically, E2F7 was confirmed as a direct target of miR-5100 using the reporter gene assay and RIP assay. By conducting flow cytometry analysis, cell cycle progression was blocked at the S phase. E2F7 overexpression partially mitigated the suppressive impact of miR-5100 in PCa cells. In conclusion, miR-5100 is a tumor suppressor in PCa by blocking cell cycle and targeting E2F7.