Attenuation Tomography Using Low-Frequency Ultrasound for Thorax Imaging: Feasibility Study.

Low-frequency ultrasound can permeate human thorax and can be applied in functional imaging of the respiratory system. In this study, we investigate the transmission of low-frequency ultrasound through the human thorax and propose a waveform matching method to track the changes in the transmission signal during subject's respiration. The method's effectiveness is validated through experiments involving ten human subjects. Furthermore, the experimental findings indicate that the traveltime of the first-arrival signal remains consistent throughout the respiratory cycle. Leveraging this observation, we introduce an algorithm for ultrasound thorax attenuation factor differential imaging. By computing the paths and energy variation of the first-arrival signal from the received waveform, the algorithm reconstructs the distribution of attenuation factor differences between two different thorax states, providing insights into the functional status of the respiratory system. Numerical experiments, using both normal thorax and defective thorax models, confirm the algorithm's feasibility and its robustness against noise, variations in transducer position and orientation. These results highlight the potential of low-frequency ultrasound for bedside, continuous monitoring of human respiratory system through functional imaging.

Ni(OH)2 Nanosheet as an Efficient Cocatalyst for Improved Photocatalytic Hydrogen Evolution over Cd0.9Zn0.1S Nanorods under Visible Light.

Loading cocatalysts to promote spatial charge separation has been confirmed as an effective method for improving photocatalytic hydrogen production. This article reports that the synthesis of Ni(OH)2/Cd0.9Zn0.1S nanorod photocatalyst is suitable for photocatalytic H2 generation under visible light. It can be proven that the binary photocatalyst exhibits a one-dimensional nanorod morphological structure. Ni(OH)2 nanosheets occupy the top area of Cd0.9Zn0.1S nanorods. The photocatalytic H2 production rate can reach 132.93 mmol·h-1·g-1, which corresponds to an apparent quantum efficiency of up to 76.5% at a wavelength of 460 nm. In addition, the Ni(OH)2 nanosheet can aggregate the light-incited electrons of Cd0.9Zn0.1S, inhibiting the confluence of electrons and holes. The detailed analysis of its mechanism through characterization methods such as photoluminescence and electrochemical measurement shows that the significant improvement in photocatalytic performance derives from the effective spatial separation of photo-induced charge carriers. Therefore, this synthesis strategy of one-dimensional materials may bring new prospects for more efficient, stable, and sustainable photocatalysis for water splitting.

Three Dimensional Microwave Data Inversion in Feature Space for Stroke Imaging.

Microwave imaging is a promising method for early diagnosing and monitoring brain strokes. It is portable, non-invasive, and safe to the human body. Conventional techniques solve for unknown electrical properties represented as pixels or voxels, but often result in inadequate structural information and high computational costs. We propose to reconstruct the three dimensional (3D) electrical properties of the human brain in a feature space, where the unknowns are latent codes of a variational autoencoder (VAE). The decoder of the VAE, with prior knowledge of the brain, acts as a module of data inversion. The codes in the feature space are optimized by minimizing the misfit between measured and simulated data. A dataset of 3D heads characterized by permittivity and conductivity is constructed to train the VAE. Numerical examples show that our method increases structural similarity by 14% and speeds up the solution process by over 3 orders of magnitude using only 4.8% number of the unknowns compared to the voxel-based method. This high-resolution imaging of electrical properties leads to more accurate stroke diagnosis and offers new insights into the study of the human brain.

Spatio-Temporal Classification of Lung Ventilation Patterns using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation.

The Pulmonary Function Test (PFT) is a widely utilized and rigorous classification test for evaluating lung function, serving as a comprehensive diagnostic tool for lung conditions. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder (VAE) with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then stacked to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data from 137 subjects were utilized for the training phase. Initially, the model underwent validation through a leave-one-out cross-validation process. During this validation, the model achieved an accuracy and sensitivity of 0.96 and 1.00, respectively, with an f1-score of 0.98 when identifying the normal subjects. To assess pipeline reliability and feasibility, we tested it on 9 newly recruited subjects, with accurate ventilation mode predictions for 8 out of 9. In addition, we included 2D EIT results for comparison and conducted ablation experiments to validate the effectiveness of the VAE. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.

Etiological relationship between lipid metabolism and endometrial carcinoma.

Endometrial carcinoma (EC) has become one of the most common gynecological malignant neoplasms in developed countries worldwide. Studies have shown that this may be closely related to the abnormal metabolism of blood lipids, which was the most significant metabolic change in the human body in this cancer. In this review, we focus on the correlation between lipid metabolism and EC and discuss the evidence that abnormal lipid metabolism promotes an increase in EC growth and metabolism, as well as the regulatory mechanism and related signaling pathways involved in this relationship. In addition, we also discussed the research progress of targeted therapies and drug treatments for EC that act on lipid metabolism, and statins are expected to become adjuvant drugs for EC in the future. This review will provide a systematic view for a better understanding of the etiological relationship between lipid metabolism and EC and further open up new therapeutic possibilities and effective treatments for EC by targeting lipid metabolism.

Risk factors for stroke in a population of central China: A cross-sectional study.

The incidence and mortality rates of stroke in China are higher than the world average, seriously endangering the public's health and quality of life. It is important to predict the incidence of stroke, identify the high-risk factors in the region, and raise the risk awareness of high-risk groups. This study sought to investigate and analyze the distribution of stroke population and the main risk factors for stroke occurrence in a Chinese population, and to predict the probability of stroke occurrence in high-risk groups with risk factors, so as to provide a scientific basis for the comprehensive prevention and treatment of stroke. A whole-group sampling method was used to investigate 1009 participants in Jingzhou city in central China, and a uniform questionnaire survey and related medical examinations were conducted. The risk factors for stroke in the area were analyzed by univariate analysis, and a multifactorial logistic regression prediction model was established based on the results of univariate analysis. The results of univariate and multifactorial logistic regression analyses suggested that gender, age, family history of stroke, hypertension, atrial fibrillation, diabetes, and sedentary lifestyle were significantly associated with an increased risk of stroke in the local population (all P < .05). The top 5 risk factors for stroke were atrial fibrillation (odds ratio [OR] = 5.225, 95% confidence interval [CI]: 2.826-9.663), sedentary lifestyle (OR = 2.701, 95% CI: 1.667-4.376), age (≥65 years) (OR = 2.593, 95% CI: 1.680-4.004), hypertension (OR = 2.106, 95% CI: 1.380-3.216), and gender (male) (OR = 2.099, 95% CI: 1.270-3.471). This study effectively identifies the high risk factors for stroke and provides scientific insights for risk assessment, intervention of risk factors, and decision making of health management departments in the central region of China. The modifable risk factors for stroke such as smoking, hypertension, atrial fibrillation, diabetes mellitus, and sedentary lifestyle were also observed. Our findings further highlight the significant of the primary and secondary prevention for stroke and reveal the potential targets to reduce the heavy stroke burden in China around the world.

Deep feature-domain matching for cardiac-related component separation from a chest electrical impedance tomography image series: proof-of-concept study.

Objectives.The cardiac-related component in chest electrical impedance tomography (EIT) measurement is of potential value to pulmonary perfusion monitoring and cardiac function measurement. In a spontaneous breathing case, cardiac-related signals experience serious interference from ventilation-related signals. Traditional cardiac-related signal-separation methods are usually based on certain features of signals. To further improve the separation accuracy, more comprehensive features of the signals should be exploited.Approach.We propose an unsupervised deep-learning method called deep feature-domain matching (DFDM), which exploits the feature-domain similarity of the desired signals and the breath-holding signals. This method is characterized by two sub-steps. In the first step, a novel Siamese network is designed and trained to learn common features of breath-holding signals; in the second step, the Siamese network is used as a feature-matching constraint between the separated signals and the breath-holding signals.Main results.The method is first tested using synthetic data, and the results show satisfactory separation accuracy. The method is then tested using the data of three patients with pulmonary embolism, and the consistency between the separated images and the radionuclide perfusion scanning images is checked qualitatively.Significance.The method uses a lightweight convolutional neural network for fast network training and inference. It is a potential method for dynamic cardiac-related signal separation in clinical settings.

Supervised Descent Learning for Thoracic Electrical Impedance Tomography.

OBJECTIVE: The absolute image reconstruction problem of electrical impedance tomography (EIT) is ill-posed. Traditional methods usually solve a nonlinear least squares problem with some kind of regularization. These methods suffer from low accuracy, poor anti-noise performance, and long computation time. Besides, the integration of a priori information is not very flexible. This work tries to solve EIT inverse problem using a machine learning algorithm for the application of thorax imaging. METHODS: We developed the supervised descent learning EIT (SDL-EIT) inversion algorithm based on the idea of supervised descent method (SDM). The algorithm approximates the mapping from measured data to the conductivity image by a series of descent directions learned from training samples. We designed a training data set in which the thorax contour, and some general structure of lungs, and heart are embedded. The algorithm is implemented in both two-, and three-dimensional cases, and is evaluated using synthetic, and measured thoracic data. Results, and conclusion: For synthetic data, SDL-EIT shows better accuracy, and anti-noise performance compared with traditional Gauss-Newton inversion (GNI) method. For measured data, the result of SDL-EIT is reasonable compared with computed tomography (CT) scan image. SIGNIFICANCE: Using SDL-EIT, prior information can be easily integrated through the specifically designed training data set, and the image reconstruction process can be accelerated. The algorithm is effective in inverting measured thoracic data. It is a potential algorithm for human thorax imaging.

Risk factors for the critical illness in SARS-CoV-2 infection: a multicenter retrospective cohort study.

BACKGROUND: Prior studies reported that 5 ~ 32% COVID-19 patients were critically ill, a situation that poses great challenge for the management of the patients and ICU resources. We aim to identify independent risk factors to serve as prediction markers for critical illness of SARS-CoV-2 infection. METHODS: Fifty-two critical and 200 non-critical SARS-CoV-2 nucleic acid positive patients hospitalized in 15 hospitals outside Wuhan from January 19 to March 6, 2020 were enrolled in this study. Multivariable logistic regression and LASSO logistic regression were performed to identify independent risk factors for critical illness. RESULTS: Age older than 60 years, dyspnea, respiratory rate > 24 breaths per min, leukocytosis > 9.5 × 109/L, neutrophilia > 6.3 × 109/L, lymphopenia < 1.1 × 109/L, neutrophil-to-lymphocyte ratio > 3.53, fibrinogen > 4 g/L, d-dimer > 0.55 µg/mL, blood urea nitrogen > 7.1 mM, elevated aspartate transaminase, elevated alanine aminotransferase, total bilirubin > 21 µM, and Sequential Organ Failure Assessment (SOFA) score ≥ 2 were identified as risk factors for critical illness. LASSO logistic regression identified the best combination of risk factors as SOFA score, age, dyspnea, and leukocytosis. The Area Under the Receiver-Operator Curve values for the risk factors in predicting critical illness were 0.921 for SOFA score, 0.776 for age, 0.764 for dyspnea, 0.658 for leukocytosis, and 0.960 for the combination of the four risk factors. CONCLUSIONS: Our findings advocate the use of risk factors SOFA score ≥ 2, age > 60, dyspnea and leukocytosis > 9.5 × 109/L on admission, alone or in combination, to determine the optimal management of the patients and health care resources.

Long non­coding RNA NEAT1 modifies cell proliferation, colony formation, apoptosis, migration and invasion via the miR­4500/BZW1 axis in ovarian cancer.

Ovarian cancer (OC) is a frequently occurring malignant tumor in women. Increasing evidence has indicated that long non­coding RNA (lncRNA) nuclear paraspeckle assembly transcript 1 (NEAT1) participates in OC pathogenesis. Thus, the aim of the present study was to explore the function of NEAT1 during OC progression. The expression levels of NEAT1, microRNA (miR)­4500 and basic leucine zipper and W2 domain­containing protein 1 (BZW1) were assessed via reverse transcription­quantitative PCR and western blotting. Furthermore, cell proliferation, colony formation, apoptosis, migration and invasion were assessed using Cell­Counting Kit 8, colony formation, flow cytometry and Transwell assays, respectively. Cell glycolysis was analyzed using an XF96 metabolic flux analyzer, and the relationship between miR­4500 and NEAT1 or BZW1 was verified via dual­luciferase reporter and RNA binding protein immunoprecipitation assays. miR­4500 expression levels were low, whereas NEAT1 expression levels were high in OC tissues and cell lines compared with control tissues and cell lines. Moreover, the results indicated that NEAT1 was a sponge of miR­4500, which directly targeted BZW1. NEAT1 knockdown induced OC cell apoptosis, and inhibited OC cell proliferation, colony formation, migration, invasion and glycolysis. miR­4500 inhibitor reversed NEAT1 knockdown­mediated effects. Similarly, miR­4500 mimic­mediated effects on cell functions were reversed by BZW1 overexpression. In addition, the results indicated that BZW1 expression was regulated by NEAT1 and miR­4500. Collectively, the present study suggested that NEAT1 modulated cell proliferation, colony formation, apoptosis, migration, invasion and glycolysis via the miR­4500/BZW1 axis in OC; therefore, NEAT1 may serve as a therapeutic target for OC.

Ag-Modified g-C3N4 Prepared by a One-Step Calcination Method for Enhanced Catalytic Efficiency and Stability.

Ag-decorated g-C3N4 (denoted as Ag/CN-x) was prepared by a one-step calcination method, and the influences of calcination time on structure, morphology, surface composition, photocatalytic performance, and catalytic reduction activity of the prepared Ag/CN-x samples were investigated. The tests showed that the Ag/CN-8 prepared through by calcination for 8 h exhibited the best photocatalytic degradation efficiency of methyl orange (98.7% within 2 h) and the best catalytic reduction property of 4-nitrophenol (100% within 70 s). Meanwhile, these Ag/CN-x samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauer-Emmett-Teller (BET), X-ray photoelectron spectroscopy (XPS), UV-vis diffuse reflectance spectra (DRS), photoluminescence (PL), photocurrent response, and electrochemical impedance spectroscopy (EIS) Nyquist plots. It was found that the Ag/CN-8 prepared through calcination for 8 h had a higher specific surface area, higher dispersibility of silver nanoparticles (Ag NPs), the widest range of visible light response, and the lowest photogenerated electron-hole recombination rate. The results of the trapping experiments indicated that a superoxide radical plays a major role. Moreover, a possible mechanism of photocatalytic degradation in methyl orange and catalytic reduction 4-nitrophenol was proposed.

Neural network-based supervised descent method for 2D electrical impedance tomography.

OBJECTIVE: In this work, we study the application of the neural network-based supervised descent method (NN-SDM) for 2D electrical impedance tomography. APPROACH: The NN-SDM contains two stages: offline training and online prediction. In the offline stage, neural networks are iteratively applied to learn a sequence of descent directions for minimizing the objective function, where the training data set is generated in advance according to prior information or historical data. In the online stage, the trained neural networks are directly used to predict the descent directions. MAIN RESULTS: Numerical and experimental results are reported to assess the efficiency and accuracy of the NN-SDM for both model-based and pixel-based inversions. In addition, the performance of the NN-SDM is compared with the linear SDM (LSDM), an end-to-end neural network (E2E-NN) and the Gauss-Newton (GN) method. The results demonstrate that the NN-SDM achieves faster convergence than the LSDM and GN method, and achieves a stronger generalization ability than the E2E-NN. SIGNIFICANCE: The NN-SDM combines the strong non-linear fitting ability of the neural network and good generalization capability of the supervised descent method (SDM), which also provides good flexibility to incorporate prior information and accelerates the convergence of iteration.

Study on 3-D Acoustic Imaging for Human Thorax Based on Contrast Source Inversion.

In this article, we study a 3-D acoustic imaging algorithm that can reconstruct compressibility, attenuation, and density simultaneously based on the contrast source inversion (CSI) method. This is a nonlinear and ill-posed inverse problem. To deal with the nonlinearity, we introduce two asymmetrical contrast sources that are functions of the contrasts and the total field. In this case, the scattered field and the total field are linear with the two contrast sources, and the two contrast sources are also linear with the two contrasts; thus, the nonlinearity is partially alleviated. To mitigate the ill-posedness of this inverse problem, we apply a multifrequency, multitransmitter, and multireceiver setting. Besides, to ensure the robustness of the algorithm, two multiplicative regularization terms are introduced as additional constraints. The reconstruction of those acoustic parameters can be achieved by alternately updating the contrast sources and the contrasts from the knowledge of the pressure field. Numerical studies show good reconstruction of compressibility, attenuation, and density of the synthetic thorax model, which validates the feasibility of imaging human thorax using low-frequency ultrasound.

Three-Dimensional Electrical Impedance Tomography With Multiplicative Regularization.

OBJECTIVE: The multiplicative regularization scheme is applied to three-dimensional electrical impedance tomography (EIT) image reconstruction problem to alleviate its ill-posedness. METHODS: A cost functional is constructed by multiplying the data misfit functional with the regularization functional. The regularization functional is based on a weighted L2-norm with the edge-preserving characteristic. Gauss-Newton method is used to minimize the cost functional. A method based on the discrete exterior calculus (DEC) theory is introduced to formulate the discrete gradient and divergence operators related to the regularization on unstructured meshes. RESULTS: Both numerical and experimental results show good reconstruction accuracy and anti-noise performance of the algorithm. The reconstruction results using human thoracic data show promising applications in thorax imaging. CONCLUSION: The multiplicative regularization can be applied to EIT image reconstruction with promising applications in thorax imaging. SIGNIFICANCE: In the multiplicative regularization scheme, there is no need to set an artificial regularization parameter in the cost functional. This helps to reduce the workload related to choosing a regularization parameter which may require expertise and many numerical experiments. The DEC-based method provides a systematic and rigorous way to formulate operators on unstructured meshes. This may help EIT image reconstructions using regularizations imposing structural or spatial constraints.

Feasibility study of acoustic imaging for human thorax using an acoustic contrast source inversion algorithm.

In this work, an acoustic imaging method based on contrast source inversion and its feasibility in quantitatively reconstructing compressibility, attenuation, and density of human thorax is studied. In the acoustic wave equation, the inhomogeneity in density makes the relationship between the contrasts and the total pressure highly nonlinear. To reduce this nonlinearity, two contrast sources are introduced to ensure symmetry in the equation, such that the inverse problem can be solved efficiently by alternately updating two contrast sources and two contrasts. Moreover, to improve the stability of the algorithm, the multiplicative regularization scheme with two additive regularization factors is applied. Using this algorithm, acoustic parameters of human thorax from low frequency ultrasound measurement are reconstructed. Numerical results show that the acoustic parameters of human thorax can be properly reconstructed at frequency of tens of kHz using this algorithm.

A Passive Temperature-Sensing Antenna Based on a Bimetal Strip Coil.

A passive temperature-sensing antenna is presented in this paper, which consists of a meandering dipole, a bimetal strip and a back cavity. The meandering dipole is divided into two parts: the lower feeding part and the upper radiating part, which maintain electric contact during operation. As a sensing component, a bimetal strip coil offers a twisting force to rotate the lower feeding part of the antenna when the temperature varies. As a result, the effective length of the dipole antenna changes, leading to a shift of the resonant frequency. Furthermore, a metal back cavity is added to increase the antenna's quality factor Q, which results in a high-sensitivity design. An antenna prototype is designed, fabricated, and measured, which achieves a sensitivity larger than 4.00 MHz/°C in a temperature range from 30 °C to 50 °C and a read range longer than 4 m. Good agreement between the simulation and measurement results is obtained.

A programmable metasurface with dynamic polarization, scattering and focusing control.

Diverse electromagnetic (EM) responses of a programmable metasurface with a relatively large scale have been investigated, where multiple functionalities are obtained on the same surface. The unit cell in the metasurface is integrated with one PIN diode, and thus a binary coded phase is realized for a single polarization. Exploiting this anisotropic characteristic, reconfigurable polarization conversion is presented first. Then the dynamic scattering performance for two kinds of sources, i.e. a plane wave and a point source, is carefully elaborated. To tailor the scattering properties, genetic algorithm, normally based on binary coding, is coupled with the scattering pattern analysis to optimize the coding matrix. Besides, inverse fast Fourier transform (IFFT) technique is also introduced to expedite the optimization process of a large metasurface. Since the coding control of each unit cell allows a local and direct modulation of EM wave, various EM phenomena including anomalous reflection, diffusion, beam steering and beam forming are successfully demonstrated by both simulations and experiments. It is worthwhile to point out that a real-time switch among these functionalities is also achieved by using a field-programmable gate array (FPGA). All the results suggest that the proposed programmable metasurface has great potentials for future applications.

Genetic ablation of Smoothened in pancreatic fibroblasts increases acinar-ductal metaplasia.

The contribution of the microenvironment to pancreatic acinar-to-ductal metaplasia (ADM), a preneoplastic transition in oncogenic Kras-driven pancreatic cancer progression, is currently unclear. Here we show that disruption of paracrine Hedgehog signaling via genetic ablation of Smoothened (Smo) in stromal fibroblasts in a Kras(G12D) mouse model increased ADM. Smo-deleted fibroblasts had higher expression of transforming growth factor-α (Tgfa) mRNA and secreted higher levels of TGFα, leading to activation of EGFR signaling in acinar cells and increased ADM. The mechanism involved activation of AKT and noncanonical activation of the GLI family transcription factor GLI2. GLI2 was phosphorylated at Ser230 in an AKT-dependent fashion and directly regulated Tgfa expression in fibroblasts lacking Smo Additionally, Smo-deleted fibroblasts stimulated the growth of Kras(G12D)/Tp53(R172H) pancreatic tumor cells in vivo and in vitro. These results define a non-cell-autonomous mechanism modulating Kras(G12D)-driven ADM that is balanced by cross-talk between Hedgehog/SMO and AKT/GLI2 pathways in stromal fibroblasts.

Cognitive impairment and gray matter volume abnormalities in silent cerebral infarction.

To investigate the association between cognitive impairment and gray matter volume (GMV) abnormalities in silent cerebral infarction (SCI) patients, the GMV of 62 pairs of patients and well-matched healthy controls was calculated. All participants underwent a P300 test, a Montreal Cognitive Assessment (MoCA) test. Compared with controls, the patients showed decreased GMV in the left superior frontal gyrus, left inferior frontal gyrus, left superior temporal gyrus, right middle temporal gyrus, and bilateral parahippocampal gyrus; no significantly increasing GMV was found. The volumes of the frontal and temporal lobes were positively correlated with the score of the MoCA scale and P300 amplitudes (r≥0.62, P<0.01). The P300 latency was negatively correlated with the volumes of the frontal lobe, the temporal lobe, and the hippocampus (r≤-0.71, P<0.05). No significant correlations between the GMV of the abnormal brain regions and four clinical characteristics in SCI patients were found, suggesting that cognitive deficiency existed in SCI patients and the reduced GMV might contribute to the pathology of cognitive deficiency in SCI patients.