Gray matter alterations in Huntington's disease: A meta-analysis of VBM neuroimaging studies.

Increasing neuroimaging studies have attempted to identify biomarkers of Huntington's disease (HD) progression. Here, we conducted voxel-based meta-analyses of voxel-based morphometry (VBM) studies on HD to investigate the evolution of gray matter volume (GMV) alterations and explore the effects of genetic and clinical features on GMV changes. A systematic review was performed to identify the relevant studies. Meta-analyses of whole-brain VBM studies were performed to assess the regional GMV changes in all HD mutation carriers, in presymptomatic HD (pre-HD), and in symptomatic HD (sym-HD). A quantitative comparison was performed between pre-HD and sym-HD. Meta-regression analyses were used to explore the effects of genetic and clinical features on GMV changes. Twenty-eight studies were included, comparing a total of 1811 HD mutation carriers [including 1150 pre-HD and 560 sym-HD] and 969 healthy controls (HCs). Pre-HD showed decreased GMV in the bilateral caudate nuclei, putamen, insula, anterior cingulate/paracingulate gyri, middle temporal gyri, and left dorsolateral superior frontal gyrus compared with HCs. Compared with pre-HD, GMV decrease in sym-HD extended to the bilateral median cingulate/paracingulate gyri, Rolandic operculum and middle occipital gyri, left amygdala, and superior temporal gyrus. Meta-regression analyses found that age, mean lengths of CAG repeats, and disease burden were negatively associated with GMV atrophy of the bilateral caudate and right insula in all HD mutation carriers. This meta-analysis revealed the pattern of GMV changes from pre-HD to sym-HD, prompting the understanding of HD progression. The pattern of GMV changes may be biomarkers for disease progression in HD.

Reconstructing Cancellous Bone From Down-Sampled Optical-Resolution Photoacoustic Microscopy Images With Deep Learning.

OBJECTIVE: Bone diseases deteriorate the microstructure of bone tissue. Optical-resolution photoacoustic microscopy (OR-PAM) enables high spatial resolution of imaging bone tissues. However, the spatiotemporal trade-off limits the application of OR-PAM. The purpose of this study was to improve the quality of OR-PAM images without sacrificing temporal resolution. METHODS: In this study, we proposed the Photoacoustic Dense Attention U-Net (PADA U-Net) model, which was used for reconstructing full-scanning images from under-sampled images. Thereby, this approach breaks the trade-off between imaging speed and spatial resolution. RESULTS: The proposed method was validated on resolution test targets and bovine cancellous bone samples to demonstrate the capability of PADA U-Net in recovering full-scanning images from under-sampled OR-PAM images. With a down-sampling ratio of [4, 1], compared to bilinear interpolation, the Peak Signal-to-Noise Ratio and Structural Similarity Index Measure values (averaged over the test set of bovine cancellous bone) of the PADA U-Net were improved by 2.325 dB and 0.117, respectively. CONCLUSION: The results demonstrate that the PADA U-Net model reconstructed the OR-PAM images well with different levels of sparsity. Our proposed method can further facilitate early diagnosis and treatment of bone diseases using OR-PAM.

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

OBJECTIVES: To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. METHODS: This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C-means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators. RESULTS: The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001). CONCLUSION: These findings suggest that fuzzy C-means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.

DF-GAM: Cross-Domain Ultrasound Image High-Quality Reconstruction Using a Dual Frequency-Domain Guided Adaptation Model.

OBJECTIVE: To enhance the quality of low-resolution (LR) ultrasound images and mitigate artifacts and speckle noise, which can impede accurate medical diagnosis, a novel method called the dual frequency-domain guided adaptation model (DF-GAM) is proposed. The method aims to achieve high-quality image reconstruction across diverse domains, including different ultrasound machines, diseases and phantom images. METHODS: DF-GAM utilizes a dual-branch network architecture combined with frequency-domain self-adaptation and self-supervised edge regression. This approach enables cross-domain enhancement by focusing on the reconstruction of clear tissue structures and speckle patterns. The model is designed to adapt to various ultrasound imaging (USI) scenarios, ensuring its applicability in real-world clinical settings. RESULTS: Experimental evaluations of DF-GAM were conducted using five different datasets. The results demonstrated the method's effectiveness, with DF-GAM outperforming existing enhancement techniques. The average peak signal-to-noise ratio (PSNR) achieved was 34.62, and the structural similarity index (SSIM) was 0.91, indicating a significant improvement in image quality compared to other methods. CONCLUSION: DF-GAM shows great potential in improving medical image diagnosis and interpretation. Its ability to enhance LR ultrasound images across various domains without the need for extensive training data makes it a valuable tool for clinical use. The high PSNR and SSIM scores validate the method's effectiveness, suggesting that DF-GAM could significantly contribute to the field of USI diagnostics.

Data-driven method of super-resolution image recovery for speckle-illumination photoacoustic computed tomography.

Methods have been proposed in recent years aimed at pushing photoacoustic imaging resolution beyond the acoustic diffraction limit, among which those based on random speckle illumination show particular promise. In this Letter, we propose a data-driven deep learning approach to processing the added spatiotemporal information resulting from speckle illumination, where the neural network learns the distribution of absorbers from a series of different samplings of the imaged area. In ex-vivo experiments based on the tomography configuration with prominent artifacts, our method successfully breaks the acoustic diffraction limit and delivers better results in identifying individual targets when compared against a selection of other leading methods.

Super-resolution reconstruction of ultrasound image using a modified diffusion model.

Objective. This study aims to perform super-resolution (SR) reconstruction of ultrasound images using a modified diffusion model, designated as the diffusion model for ultrasound image super-resolution (DMUISR). SR involves converting low-resolution images to high-resolution ones, and the proposed model is designed to enhance the suitability of diffusion models for this task in the context of ultrasound imaging.Approach. DMUISR incorporates a multi-layer self-attention (MLSA) mechanism and a wavelet-transform based low-resolution image (WTLR) encoder to enhance its suitability for ultrasound image SR tasks. The model takes interpolated and magnified images as input and outputs high-quality, detailed SR images. The study utilized 1,334 ultrasound images from the public fetal head-circumference dataset (HC18) for evaluation.Main results. Experiments were conducted at 2× , 4× , and 8×  magnification factors. DMUISR outperformed mainstream ultrasound SR methods (Bicubic, VDSR, DECUSR, DRCN, REDNet, SRGAN) across all scales, providing high-quality images with clear structures and rich detailed textures in both hard and soft tissue regions. DMUISR successfully accomplished multiscale SR reconstruction while suppressing over-smoothing and mode collapse problems. Quantitative results showed that DMUISR achieved the best performance in terms of learned perceptual image patch similarity, with a significant decrease of over 50% at all three magnification factors (2× , 4× , and 8× ), as well as improvements in peak signal-to-noise ratio and structural similarity index measure. Ablation experiments validated the effectiveness of the MLSA mechanism and WTLR encoder in improving DMUISR's SR performance. Furthermore, by reducing the number of diffusion steps, the computational time of DMUISR was shortened to nearly one-tenth of its original while maintaining image quality without significant degradation.Significance. This study demonstrates that the modified diffusion model, DMUISR, provides superior performance for SR reconstruction of ultrasound images and has potential in improving imaging quality in the medical ultrasound field.

The Protocol of Ultrasonic Backscatter Measurements of Musculoskeletal Properties.

This study aims to introduce the protocol for ultrasonic backscatter measurements of musculoskeletal properties based on a novel ultrasonic backscatter bone diagnostic (UBBD) instrument. Dual-energy X-ray absorptiometry (DXA) can be adopted to measure bone mineral density (BMD) in the hip, spine, legs and the whole body. The muscle and fat mass in the legs and the whole body can be also calculated by DXA body composition analysis. Based on the proposed protocol for backscatter measurements by UBBD, ultrasonic backscatter signals can be measured in vivo, deriving three backscatter parameters [apparent integral backscatter (AIB), backscatter signal peak amplitude (BSPA) and the corresponding arrival time (BSPT)]. AIB may provide important diagnostic information about bone properties. BSPA and BSPT may be important indicators of muscle and fat properties. The standardized backscatter measurement protocol of the UBBD instrument may have the potential to evaluate musculoskeletal characteristics, providing help for promoting the application of the backscatter technique in the clinical diagnosis of musculoskeletal disorders (MSDs), such as osteoporosis and muscular atrophy.

Backscatter measurement of cancellous bone using the ultrasound transit time spectroscopy.

Recently, ultrasound transit time spectroscopy (UTTS) was proposed as a promising method for bone quantitative ultrasound measurement. Studies have showed that UTTS could estimate the bone volume fraction and other trabecular bone structure in ultrasonic through-transmission measurements. The goal of this study was to explore the feasibility of UTTS to be adapted in ultrasonic backscatter measurement and further evaluate the performance of backscattered ultrasound transit time spectrum (BS-UTTS) in the measurement of cancellous bone density and structure. First, taking ultrasonic attenuation into account, the concept of BS-UTTS was verified on ultrasonic backscatter signals simulated from a set of scatterers with different positions and intensities. Then, in vitro backscatter measurements were performed on 26 bovine cancellous bone specimens. After a logarithmic compression of the BS-UTTS, a linear fitting of the log-compressed BS-UTTS versus ultrasonic propagated distance was performed and the slope and intercept of the fitted line for BS-UTTS were determined. The associations between BS-UTTS parameters and cancellous bone features were analyzed using simple linear regression. The results showed that the BS-UTTS could make an accurate deconvolution of the backscatter signal and predict the position and intensity of the simulated scatterers eliminating phase interference, even the simulated backscatter signal was with a relatively low signal-to-noise ratio. With varied positions and intensities of the scatterers, the slope of the fitted line for the log-compressed BS-UTTS versus ultrasonic propagated distance (i.e., slope of BS-UTTS for short) yield a high agreement (r2 = 99.84%-99.96%) with ultrasonic attenuation in simulated backscatter signal. Compared with the high-density cancellous bone, the low-density specimen showed more abundant backscatter impulse response in the BS-UTTS. The slope of BS-UTTS yield a significant correlation with bone mineral density (r = 0.87; p < 0.001), BV/TV (r = 0.87; p < 0.001), and cancellous bone microstructures (r up to 0.87; p < 0.05). The intercept of BS-UTTS was also significantly correlated with bone densities (r = -0.87; p < 0.001) and trabecular structures (|r|=0.43-0.80; p < 0.05). However, the slope of the BS-UTTS underestimated attenuation when measurements were performed experimentally. In addition, a significant non-linear relationship was observed between the measured attenuation and the attenuation estimated by the slope of the BS-UTTS. This study demonstrated that the UTTS method could be adapted to ultrasonic backscatter measurement of cancellous bone. The derived slope and intercept of BS-UTTS could be used in the measurement of bone density and microstructure. The backscattered ultrasound transit time spectroscopy might have potential in the diagnosis of osteoporosis in the clinic.

Non-Volatile Component and Antioxidant Activity: A Comparative Analysis between Litsea cubeba Branches and Leaves.

Litsea cubeba, which is found widely distributed across the Asian region, functions as both an economic tree and a medicinal plant with a rich historical background. Previous investigations into its chemical composition and biological activity have predominantly centered on volatile components, leaving the study of non-volatile components relatively unexplored. In this study, we employed UPLC-HRMS technology to analyze the non-volatile components of L. cubeba branches and leaves, which successfully resulted in identifying 72 constituents. Comparative analysis between branches and leaves unveiled alkaloids, organic acids, and flavonoids as the major components. However, noteworthy differences in the distribution of these components between branches and leaves were observed, with only eight shared constituents, indicating substantial chemical variations in different parts of L. cubeba. Particularly, 24 compounds were identified for the first time from this plant. The assessment of antioxidant activity using four methods (ABTS, DPPH, FRAP, and CUPRAC) demonstrated remarkable antioxidant capabilities in both branches and leaves, with slightly higher efficacy observed in branches. This suggests that L. cubeba may act as a potential natural antioxidant with applications in health and therapeutic interventions. In conclusion, the chemical composition and antioxidant activity of L. cubeba provides a scientific foundation for its development and utilization in medicine and health products, offering promising avenues for the rational exploitation of L. cubeba resources in the future.

B-mode ultrasound to elastography synthesis using multiscale learning.

Elastography is a promising diagnostic tool that measures the hardness of tissues, and it has been used in clinics for detecting lesion progress, such as benign and malignant tumors. However, due to the high cost of examination and limited availability of elastic ultrasound devices, elastography is not widely used in primary medical facilities in rural areas. To address this issue, a deep learning approach called the multiscale elastic image synthesis network (MEIS-Net) was proposed, which utilized the multiscale learning to synthesize elastic images from ultrasound data instead of traditional ultrasound elastography in virtue of elastic deformation. The method integrates multi-scale features of the prostate in an innovative way and enhances the elastic synthesis effect through a fusion module. The module obtains B-mode ultrasound and elastography feature maps, which are used to generate local and global elastic ultrasound images through their correspondence. Finally, the two-channel images are synthesized into output elastic images. To evaluate the approach, quantitative assessments and diagnostic tests were conducted, comparing the results of MEIS-Net with several deep learning-based methods. The experiments showed that MEIS-Net was effective in synthesizing elastic images from B-mode ultrasound data acquired from two different devices, with a structural similarity index of 0.74 ± 0.04. This outperformed other methods such as Pix2Pix (0.69 ± 0.09), CycleGAN (0.11 ± 0.27), and StarGANv2 (0.02 ± 0.01). Furthermore, the diagnostic tests demonstrated that the classification performance of the synthetic elastic image was comparable to that of real elastic images, with only a 3 % decrease in the area under the curve (AUC), indicating the clinical effectiveness of the proposed method.

Diagnosis of cognitive and motor disorders levels in stroke patients through explainable machine learning based on MRI.

BACKGROUND: Globally, stroke is the third most significant cause of disability. A stroke may produce motor, sensory, perceptual, or cognitive disorders that result in disability and affect the likelihood of recovery, affecting a person's ability to function. Evaluation post-stroke is critical for optimal stroke care. PURPOSE: Traditional methods for classifying the clinical disorders of cognitive and motor in stroke patients use assessment and interrogative measures, which are time-consuming, complex, and labor-intensive. In response to the current situation, this study develops an algorithm to automatically classify motor and cognitive disorders in stroke patients by 3D brain MRI to assist physicians in diagnosis. METHODS: First, radiomics and fusion features are extracted from the OAx T2 Propeller of 3D brain MRI. Then, we use 14 machine learning models and one model ensemble method to predict Fugl-Meyer and MMSE levels of stroke patients. Next, we evaluate the models using accuracy, recall, f1-score, and area under the curve (AUC). Finally, we employ SHAP to explain the output of the model. RESULTS: The best predictive models come from Random Forest (RF) Classifier with fusion features in cognitive classification and Linear Discriminant Analysis (LDA) with radiomics features in motor classification. The highest accuracies are 92.0 and 82.5% for cognitive and motor disorders. CONCLUSIONS: MRI brain maps can classify the cognitive and motor disorders of stroke patients. Radiomics features demonstrate its merits. The proposed algorithms with MRI images can efficiently assist physicians in diagnosing the cognitive and motor disorders of stroke patients in clinical practice. Additionally, this lessens labor costs, improves diagnostic effectiveness, and avoids the subjective difference that comes with manual assessment.

Transition Metal Oxide-Decorated MXenes as Drugless Nanoarchitectonics for Enriched Nanocatalytic Chemodynamic Treatment.

Despite their unique characteristics, 2D MXenes with sole photothermal conversion ability are required to explore their superfluous abilities in biomedicine. The small-molecule-based chemotherapeutics suffer from various shortcomings of time-consuming and expensiveness concerning theoretical and performance (preclinical/clinical) checks. This study demonstrates the fabrication of Ti3C2 MXene nanosheets (TC-MX NSs) and subsequent decoration with transition metal oxides, that is, copper oxide (Cu2O/MX, CO-MX NCs) as drugless nanoarchitectonics for synergistic photothermal (PTT)-chemodynamic therapeutic (CDT) efficacies. Initially, the monolayer/few-layered TC-MX NSs are prepared using the chemical etching-assisted ultrasonic exfoliation method and then deposited with Cu2O nanoconstructs using the in situ reduction method. Further, the photothermal ablation under near-infrared (NIR)-II laser irradiation shows PTT effects of CO-MX NCs. The deposited Cu2O on TC-MX NSs facilitates the release of copper (Cu+) ions in the acidic microenvironment intracellularly for Fenton-like reaction-assisted CDT effects and enriched PTT effects synergistically. Mechanistically, these deadly free radicals intracellularly imbalance the glutathione (GSH) levels and result in mitochondrial dysfunction, inducing apoptosis of 4T1 cells. Finally, the in vivo investigations in BALB/c mice confirm the substantial ablation of breast carcinoma. Together, these findings demonstrate the potential synergistic PTT-CDT effects of the designed CO-MX NCs as drugless nanoarchitectonics against breast carcinoma.

A multimodal meta-analysis of gray matter alterations in trigeminal neuralgia.

Background: Brain gray matter alterations in patients with trigeminal neuralgia (TN) have been detected in prior neuroimaging studies, but the results are heterogeneous. The current study conducted coordinate-based meta-analyses across neuroimaging studies, aiming to find the pattern of brain anatomic and functional alterations in patients with TN. Methods: We performed a systematic literature search of PubMed, Embase, and Web of Science to identify relevant publications. A multimodal meta-analysis for whole-brain voxel-based morphometry (VBM) studies and functional imaging studies in TN was performed using anisotropic effect size-based signed differential mapping. Results: The meta-analysis comprised 10 VBM studies with 398 TN patients and 275 healthy controls, and 13 functional magnetic resonance imaging studies with 307 TN patients and 264 healthy controls. The multimodal meta-analysis showed conjoint structural and functional brain alterations in the right fusiform gyrus and inferior temporal gyrus, bilateral thalamus, left superior temporal gyrus, left insula, and inferior frontal gyrus. The unimodal meta-analysis showed decreased gray matter volume alone in the left putamen, left postcentral gyrus, and right amygdala as well as only functional abnormalities in the left cerebellum, bilateral precuneus, and left middle temporal gyrus. Conclusion: This meta-analysis revealed overlapping anatomic and functional gray matter abnormalities in patients with TN, which may help provide new insights into the neuropathology and potential treatment biomarkers of TN.

Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network.

Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues, playing a crucial role in the field of histopathology. However, when labeling and imaging biological tissues, there are still some challenges, e.g., time-consuming tissue preparation steps, expensive reagents, and signal bias due to photobleaching. To overcome these limitations, we present a deep-learning-based method for fluorescence translation of tissue sections, which is achieved by conditional generative adversarial network (cGAN). Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image, and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well. Moreover, this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes, and further saves the cost and time. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00094-1.

Loneliness, spiritual well-being, and death perception, as well as their risk factors in urological cancer patients.

Cancer patients commonly suffer from loneliness, poor spiritual status, and fear of death; however, these evaluations are rarely revealed in urological cancer patients. Thus, this study aimed to assess the loneliness, spiritual well-being, and death perception, as well as their risk factors in urological cancer patients. A total of 324 urological (including renal, bladder, and prostate) cancer patients and 100 healthy controls were included. The University of California and Los Angeles loneliness scale (UCLA-LS), functional assessment of chronic illness therapy-spiritual well-being (FACIT-Sp), and death attitude profile-revised (DAP-R) scores were evaluated. The results showed that the UCLA-LS score was higher, but the FACIT-Sp score was lower in urological cancer patients than in healthy controls. According to the DAP-R score, fear of death, death avoidance, and approaching death acceptance were elevated, but neutral acceptance was lower in urological cancer patients than in healthy controls. Among urological cancer patients, the UCLA-LS score was highest but the FACIT-Sp score was lowest in bladder cancer patients; regarding the DAP-R score, fear of death and death avoidance were highest, but approaching death acceptance was lowest in bladder cancer patients. Interestingly, single/divorced/widowed status, bladder cancer diagnosis, higher pathological grade, surgery, systemic treatment, and local treatment were independent factors for higher UCLA-LS score or lower FACIT-Sp score. In conclusion, urological cancer (especially bladder cancer) patients bear increased loneliness and reduced spiritual well-being; they also carry higher fear of death, death avoidance, and approaching death acceptance but lower neutral acceptance of death.

Ray theory-based compounded plane wave ultrasound imaging for aberration corrected transcranial imaging: Phantom experiments and simulations.

Compounded plane wave imaging (CPWI) allows high-frame-rate measurement and has been one of the most promising modalities for real-time brain imaging. However, ultrasonic brain imaging using the CPWI modality is usually performed with a worn thin or removal of the skull layer. Otherwise, the skull layer is expected to distort the ultrasonic wavefronts and significantly decrease intracranial imaging quality. The motivation of this study is to investigate a CPWI method for transcranial brain imaging with the skull layer. A coordinate transformation ray-tracing (CTRT) approach was proposed to track the distorted ultrasonic wavefronts and calculate the time delays for the ultrasound plane wave passing through the skull layer. With an accurate correction for the time delays in beamforming, the CTRT-based CPWI could achieve high-quality intracranial images with the presence of skulls. The proposed CTRT-based CPWI method was verified using a simplified three-layer transcranial model. The full-wave simulation demonstrated that CTRT could accurately (i.e., relative percentage error less than0.18%) track the distorted transmitting wavefront through skull. Compared with the CPWI without aberration correction, the CTRT-based CPWI provided high-quality intracranial imaging and could accurately localize intracranial point scatterers; specifically, positioning error decreases from 0.5 mm to 0.1 mm on average in the axial direction and from 0.7 mm to 0.1 mm on average in the lateral direction. As the compounded angles increased in the CTRT-based CPWI, the contrast improved by 16.2 dB on average for the region of interest, and the array performance indicator (representing resolution) decreased by 4.0 on average for the intracranial point scatterers. The CTRT is of low computational cost compared with full wave simulation. This study suggested that the proposed CTRT-based CPWI might have the potential for real-time and non-invasive transcranial aberration-corrected imaging.

Novel discovery of schisandrin A regulating the interplay of autophagy and apoptosis in oligoasthenospermia by targeting SCF/c-kit and TRPV1 via biosensors.

Oligoasthenospermia is the primary cause of infertility. However, there are still enormous challenges in the screening of critical candidates and targets of oligoasthenospermia owing to its complex mechanism. In this study, stem cell factor (SCF), c-kit, and transient receptor potential vanilloid 1 (TRPV1) biosensors were successfully established and applied to studying apoptosis and autophagy mechanisms. Interestingly, the detection limit reached 2.787 × 10-15 g/L, and the quantitative limit reached 1.0 × 10-13 g/L. Furthermore, biosensors were used to investigate the interplay between autophagy and apoptosis. Schisandrin A is an excellent candidate to form a system with c-kit similar to SCF/c-kit with a detection constant (KD) of 5.701 × 10-11 mol/L, whereas it had no affinity for SCF. In addition, it also inhibited autophagy in oligoasthenospermia through antagonizing TRPV1 with a KD of up to 4.181 × 10-10 mol/L. In addition, in vivo and in vitro experiments were highly consistent with the biosensor. In summary, high-potency schisandrin A and two potential targets were identified, through which schisandrin A could reverse the apoptosis caused by excessive autophagy during oligoasthenospermia. Our study provides promising insights into the discovery of effective compounds and potential targets via a well-established in vitro-in vivo strategy.

Hybrid nanoarchitectonics of molybdenum dioxide (MoO2) and doxorubicin (DOX) for synergistic chemo-photothermal-based breast carcinoma therapy.

Cancer has emerged as one of the severe ailments due to the uncontrolled proliferation rate of cells, accounting for millions of deaths annually. Despite the availability of various treatment strategies, including surgical interventions, radiation, and chemotherapy, tremendous advancements in the past two decades of research have evidenced the generation of different nanotherapeutic designs toward providing synergistic therapy. In this study, we demonstrate the assembly of a versatile nanoplatform based on the hyaluronic acid (HA)-coated molybdenum dioxide (MoO2) assemblies to act against breast carcinoma. The hydrothermal approach-assisted MoO2 constructs are immobilized with doxorubicin (DOX) molecules on the surface. Further, these MoO2-DOX hybrids are encapsulated with the HA polymeric framework. Furthermore, the versatile nanocomposites of HA-coated MoO2-DOX hybrids are systematically characterized using various characterization techniques, and explored biocompatibility in the mouse fibroblasts (L929 cell line), as well as synergistic photothermal (808-nm laser irradiation for 10 min, 1 W/cm2) and chemotherapeutic properties against breast carcinoma (4T1 cells). Finally, the mechanistic views concerning the apoptosis rate are explored using the JC-1 assay to measure the intracellular mitochondrial membrane potential (MMP) levels. In conclusion, these findings indicated excellent photothermal and chemotherapeutic efficacies, exploring the enormous potential of MoO2 composites against breast cancer.

A Bayesian Filtering Approach to Multimode Separation and Retrieval of Ultrasonic Guided Waves.

In this article, a Bayesian filtering approach to adaptively extracting the crossed time-frequency (TF) ridges of ultrasonic guided waves (GWs) and retrieving their overlapped modes is proposed. Based on the generalized non-parametric GW signal model, the phase evolution of each overlapped mode can be described by the state transition equation developed by a polynomial prediction model (PPM). When an analyzed GW in the frequency domain is viewed as the measurement equation of the states, a state space model in the frequency domain for describing the GW and its modes is established. As a result, a Bayesian filtering approach can be used to extract the crossed TF ridges and separate the overlapped modes in an analyzed GW when the mode number in the signal is known as a priori. When such a priori is unavailable, an adaptive detection method of the mode number in a GW is acquired by a non-parametric iterative adaptive estimation scheme. In this way, the proposed method can be applied to cases where an analyzed GW with unknown modes can also be extracted and separated accurately. Simulation results show that the proposed method can correctly extract the crossed TF ridges and separate the overlapped modes when the signal-to-noise ratio (SNR) is higher than -5 dB. In the steel plate experiment, the correlation coefficients of S0 , A0 , and A1 modes between the original and retrieved signals are 0.900, 0.772, and 0.915, respectively, which are over the reported results in the literature.

Full waveform inversion guided wave tomography with a recurrent neural network.

Corrosion quantitative detection of plate or plate-like structure materials is crucial in industrial Non-Destructive Testing (NDT) for determining their remaining life. For doing that, a novel ultrasonic guided wave tomography method, incorporating recurrent neural network (RNN) into full waveform inversion (FWI) called as RNN-FWI, is proposed in this paper. When the wave equation of an acoustic model is solved by a forward model with the cyclic calculation units of an RNN, it is shown that the inversion of the forward model can be obtained iteratively by minimizing a waveform misfit function of quadratic Wasserstein distance between the modeled and measured data. It is also demonstrated that the gradient of the objective function can be obtained by automatic differentiation while the parameters of the waveform velocity model are updated by the adaptive momentum estimation algorithm (Adam). The U-Net deep image prior (DIP) is used as the velocity model regularization in each iteration. The final thickness maps of the plate or plate-like structure materials shown can be archived by the dispersion characteristics of guided waves. Both the numerical simulation and experimental results show that the proposed RNN-FWI tomography method performs better than the conventional time-domain FWI in terms of convergence rate, initial model requirement, and robustness.