Ultrasound-targeted microbubble destruction enhances AAV mediated gene transfection: human RPE cells in vitro and the rat retina in vivo

The present study was performed to investigate the efficacy and safety of Ultrasound-targeted microbubble destruction (UTMD) mediated rAAV2-EGFP to cultured human retinal pigment epithelium (RPE) cells _in vitro_ and the rat retina _in vivo_. _In vitro_ study, cultured human RPE cells were exposed to US under different conditions with or without microbubbles. Furthermore, the effect of UTMD to rAAV2-EGFP itself and the cells were evaluated. _In vivo_ study, gene transfer was examined by injecting rAAV2-EGFP into the subretinal space of the rats with or without microbubbles and then exposed to US. We investigated EGFP expression _in vivo_ via stereomicroscopy and performed quantitative analysis by Axiovision 3.1 software. HE staining and frozen sections were used to observe tissue damage and location of EGFP gene expression. _In vitro_ study, the transfection efficiency of rAAV2-EGFP increased 74.85% under the optimal UTMD conditions. Furthermore, there was almost no cytotoxicity to the cells and rAAV2-EGFP itself. _In vivo_ study, UTMD could be used safely to enhance and accelerate transgene expression of the retina. Fluorescence expression was mainly located in the layer of retina. UTMD is a promising method for gene delivery to the retina

Global-regional nested simulation of particle number concentration by combing microphysical processes with an evolving organic aerosol module

Aerosol microphysical processes are essential for the next generation of global and regional climate and air quality models to determine particle size distribution. The contribution of organic aerosols (OAs) to particle formation, mass, and number concentration is one of the major uncertainties in current models. A new global–regional nested aerosol model was developed to simulate detailed microphysical processes. The model combines an advanced particle microphysics (APM) module and a volatility basis set (VBS) OA module to calculate the kinetic condensation of low-volatility organic compounds and equilibrium partitioning of semi-volatile organic compounds in a 3-D framework using global–regional nested domain. In addition to the condensation of sulfuric acid, the equilibrium partitioning of nitrate and ammonium, and the coagulation process of particles, the microphysical processes of the OAs are realistically represented in our new model. The model uses high-resolution size bins to calculate the size distribution of new particles formed through nucleation and subsequent growth. The multi-scale nesting enables the model to perform high-resolution simulations of the particle formation processes in the urban atmosphere in the background of regional and global environments. By using the nested domains, the model reasonably reproduced the OA components obtained from the analysis of aerosol mass spectrometry measurements through positive matrix factorization and the particle number size distribution in the megacity of Beijing during a period of approximately a month. Anthropogenic organic species accounted for 67 % of the OAs of secondary particles formed by nucleation and subsequent growth, which is considerably larger than that of biogenic OAs. On the global scale, the model well predicted the particle number concentration in various environments. The microphysical module combined with the VBS simulated the universal distribution of organic components among the different aerosol populations. The model results strongly suggest the importance of anthropogenic organic species in aerosol particle formation and growth at polluted urban sites and over the whole globe.Aerosol microphysical processes are essential for the next generation of global and regional climate and air quality models to determine particle size distribution. The contribution of organic aerosols (OAs) to particle formation, mass, and number concentration is one of the major uncertainties in current models. A new global-regional nested aerosol model was developed to simulate detailed microphysical processes. The model combines an advanced particle microphysics (APM) module and a volatility basis set (VBS) OA module to calculate the kinetic condensation of low-volatility organic compounds and equilibrium partitioning of semi-volatile organic compounds in a 3-D framework using global-regional nested domain In addition to the condensation of sulfuric acid, the equilibrium partitioning of nitrate and ammonium, and the coagulation process of particles, the microphysical processes of the OAs are realistically represented in our new model. The model uses high-resolution size bins to calculate the size distribution of new particles formed through nucleation and subsequent growth. The multi-scale nesting enables the model to perform high-resolution simulations of the particle formation processes in the urban atmosphere in the background of regional and global environments. By using the nested domains, the model reasonably reproduced the OA components obtained from the analysis of aerosol mass spectrometry measurements through positive matrix factorization and the particle number size distribution in the megacity of Beijing during a period of approximately a month. Anthropogenic organic species accounted for 67 % of the OAs of secondary particles formed by nucleation and subsequent growth, which is considerably larger than that of biogenic OAs. On the global scale, the model well predicted the particle number concentration in various environments. The microphysical module combined with the VBS simulated the universal distribution of organic components among the different aerosol populations. The model results strongly suggest the importance of anthropogenic organic species in aerosol particle formation and growth at polluted urban sites and over the whole globe.Peer reviewe

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance

ObjectiveIn physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers.MethodsFemale patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves.ResultsA total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists’ group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer.ConclusionsThe AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness

Preoperative strain ultrasound elastography can predict occult central cervical lymph node metastasis in papillary thyroid cancer: a single-center retrospective study

ObjectiveTo determine whether preoperative ultrasound elastography can predict occult central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid cancer.MethodsThis retrospective study included 541 papillary thyroid cancer patients with clinically negative lymph nodes prior to surgery between July 2019 and December 2021. Based on whether CCLNM was present on postoperative pathology, patients were categorized as CCLNM (+) or CCLNM (-). Preoperative clinical data, conventional ultrasound features, and ultrasound elastography indices were compared between the groups. Univariate and multivariate logistic regression analysis were performed to identify the independent predictors of occult CCLNM.ResultsA total of 36.60% (198/541) patients had confirmed CCLNM, while 63.40% (343/541) did not. Tumor location, bilaterality, multifocality, echogenicity, margin, shape, vascularity, capsule contact, extrathyroidal extension, aspect ratio, and shear wave elasticity parameters were comparable between the groups (all P > 0.05). Univariate analysis showed statistically significant differences between the two groups in age, sex, tumor size, calcification, capsule invasion, and strain rates ratio in strain ultrasound elastography (all P < 0.05). In multivariate logistic regression analysis, the independent predictors of occult CCLNM were age (OR = 0.975, 95% CI = 0.959-0.991, P = 0.002), sex (OR = 1.886, 95% CI = 1.220-2.915, P = 0.004), tumor size (OR = 1.054, 95% CI = 1.014-1.097, P = 0.008), and strain rates ratio (OR = 1.178, 95% CI = 1.065-1.304, P = 0.002).ConclusionPreoperative strain ultrasound elastography can predict presence of occult CCLNM in papillary thyroid cancer patients and help clinicians select the appropriate treatment strategy

A study of the effect of suboptimal glycemic control on subclinical myocardial systolic function in patients with T2DM

Objective·To explore the relationship between poor blood glucose control and early impaired cardiac function in patients with type 2 diabetes mellitus (T2DM).Methods·Eighty-three patients diagnosed with T2DM in Jiading Branch of Shanghai General Hospital from June 2021 to March 2022 were selected and divided into two groups according to the level of hemoglobin A1c (HbA1c): satisfactory control of glycaemia (SCG) group and less satisfactory control of glycaemia (LSCG) group. Fifty-four subjects were in the control group. Echocardiography was performed to obtain left ventricular structural and functional parameters and left ventricular subendocardial, medial and subepicardial global longitudinal strain (GLS): GLSendo, GLSmid, and GLSepi. The parameters were compared by using analysis of variance. The correlation analysis was performed by Pearson correlation analysis and multiple linear regression analysis. The diagnostic performance of longitudinal strain in differentiating subclinical myocardial dysfunction in patients with T2DM was analyzed by receiver operator characteristic (ROC) curve.Results·The thickness of the ventricular septum and the posterior wall of the left ventricle were thicker in the LSCG group than in the SCG group and the control group (all P0.05). Compared with the control group, the left ventricular diastolic function index E/e (early peak flow velocity by Doppler/early and atrial diastolic velocity of the mitral annulus by tissue Doppler imaging) was higher in both the LSCG group and the SCG group (all P 0.05). There was no significant difference in left ventricular ejection fraction among the three groups (P>0.05). Compared with LSCG group, GLSendo, GLSmid and GLSepi were higher in the SCG group and control group (all P0.05). HbA1c was an independently negative factor of GLSmid and GLSepi (β= -0.198 and -0.239, all P<0.05). GLSendo, GLSmid and GLSepi had moderate diagnostic performance between the LSCG group and SCG group, with areas under the curve (AUC) of 0.754 (95%CI 0.624‒0.884), 0.755 (95%CI 0.624‒0.885), and 0.751 (95%CI 0.619‒0.882), respectively.Conclusions·T2DM patients with unsatisfactory glycemic control have reduced myocardial contractility, and this subclinical myocardial damage is independently negatively correlated with the level of HbA1c

An improvement of carotid intima-media thickness and pulse wave velocity in renal transplant recipients

Abstract Background Renal transplantation can significantly improve the quality of life of patients with end stage renal disease (ESRD) who would otherwise require dialysis. Renal transplant (RT) recipients have higher risks of cardiovascular disease compared with general population. The carotid intima-media thickness (CIMT) and pulse wave velocity (PWV) have been used as the important predicting factor of vascular arteriosclerosis. Therefore, this study was to investigate the improvement of carotid intima-media thickness and pulse wave velocity in renal transplant recipients. Methods Thirty-one patients with chronic kidney disease being treated with hemodialysis, 31 renal transplant recipients and 84 healthy control subjects were included to have the clinical evaluations and ultrasonography of bilateral carotid arteries. CIMT and PWV were independently measured by two ultrasonographers using the technique of ultrasonic radiofrequency tracking and correlated with arteriosclerosis risk factors. The progression of CIMT and PWV with age were analyzed by linear regression models, and the slopes of curves were compared using Z test. Results Compared with the patients on hemodialysis, the CIMT was significantly lower in renal transplant recipients and healthy control. The PWV were higher in hemodialysis patients and renal transplant recipients than that of the subjects in control group. The progression is CIMT positively corelated with age and cumulative duration in renal transplant recipients and hemodialysis patients. In both hemodialysis patients and renal transplant recipients, age and cumulative time on dialysis were all positively correlated with the increase of PWV as well. Conclusions Carotid intima-media thickness and pulse wave velocity is the predicting factors of developing arteriosclerosis, which were improved in renal transplant recipients

An Improved Fractional-Order Optical Flow Model for Motion Estimation

The Horn and Schunck (HS) optical flow model cannot preserve discontinuity of motion estimation and has low accuracy especially for the image sequence, which includes complex texture. To address this problem, an improved fractional-order optical flow model is proposed. In particular, the fractional-order Taylor series expansion is applied in the brightness constraint equation of the HS model. The fractional-order flow field derivative is also used in the smoothing constraint equation. The Euler-Lagrange equation is utilized for the minimization of the energy function of the fractional-order optical flow model. Two-dimensional fractional differential masks are proposed and applied to the calculation of the model simplification. Considering the spatiotemporal memory property of fractional-order, the algorithm preserves the edge discontinuity of the optical flow field while improving the accuracy of the estimation of the dense optical flow field. Experiments on Middlebury datasets demonstrate the predominance of our proposed algorithm

Lightweight Oriented Detector for Insulators in Drone Aerial Images

Due to long-term exposure to the wild, insulators are prone to various defects that affect the safe operation of the power system. In recent years, the combination of drones and deep learning has provided a more intelligent solution for insulator automatic defect inspection. Positioning insulators is an important prerequisite step for defect detection, and the accuracy of insulator positioning greatly affects defect detection. However, traditional horizontal detectors lose directional information and it is difficult to accurately locate tilted insulators. Although oriented detectors can predict detection boxes with rotation angles to solve this problem, these models are complex and difficult to apply to edge devices with limited computing power. This greatly limits the practical application of deep learning methods in insulator detection. To address these issues, we proposed a lightweight insulator oriented detector. First, we designed a lightweight insulator feature pyramid network (LIFPN). It can fuse features more efficiently while reducing the number of parameters. Second, we designed a more lightweight insulator oriented detection head (LIHead). It has less computational complexity and can predict rotated detection boxes. Third, we deployed the detector on edge devices and further improved its inference speed through TensorRT. Finally, a series of experiments demonstrated that our method could reduce the computational complexity of the detector by approximately 49 G and the number of parameters by approximately 30 M while ensuring almost no decrease in the detection accuracy. It can be easily deployed to edge devices and achieve a detection speed of 41.89 frames per second (FPS)

Avionics Module Fault Diagnosis Algorithm Based on Hybrid Attention Adaptive Multi-Scale Temporal Convolution Network

Since the reliability of the avionics module is crucial for aircraft safety, the fault diagnosis and health management of this module are particularly significant. While deep learning-based prognostics and health management (PHM) methods exhibit highly accurate fault diagnosis, they have disadvantages such as inefficient data feature extraction and insufficient generalization capability, as well as a lack of avionics module fault data. Consequently, this study first employs fault injection to simulate various fault types of the avionics module and performs data enhancement to construct the P2020 communications processor fault dataset. Subsequently, a multichannel fault diagnosis method, the Hybrid Attention Adaptive Multi-scale Temporal Convolution Network (HAAMTCN) for the integrated functional circuit module of the avionics module, is proposed, which adaptively constructs the optimal size of the convolutional kernel to efficiently extract features of avionics module fault signals with large information entropy. Further, the combined use of the Interaction Channel Attention (ICA) module and the Hierarchical Block Temporal Attention (HBTA) module results in the HAAMTCN to pay more attention to the critical information in the channel dimension and time step dimension. The experimental results show that the HAAMTCN achieves an accuracy of 99.64% in the avionics module fault classification task which proves our method achieves better performance in comparison with existing methods

A combination of ultrasound-targeted microbubble destruction with transplantation of bone marrow mesenchymal stem cells promotes recovery of acute liver injury

Abstract Background Bone marrow mesenchymal stem cells (BMSCs) can provide an additional source of therapeutic stem cells for regeneration of liver cells during acute liver injury (ALI). However, the insufficient hepatic homing by the transplanted BMSCs limits their applications. Ultrasound-targeted microbubble destruction (UTMD) has been reported to promote the homing of transplanted stem cells into the ischemic myocardium. In this study, we investigated whether UTMD promotes the hepatic homing of BMSCs in ALI rats and evaluated the therapeutic effect. Methods BMSCs were isolated from the femurs and tibias of Sprague-Dawley (SD) rats. The isolated BMSCs were stably transfected with a lentivirus expressing enhanced green fluorescent protein (EGFP) that can be visualized and quantified in vivo after transplantation. Both tumor necrosis factor α (TNF-α) and stromal cell-derived factor 1 (SDF-1) were used to verify the appropriate ultrasound parameters. The ALI rats were divided into four groups: control, BMSCs, UTMD, and UTMD + BMSCs. The protein and mRNA expression levels of SDF-1, intercellular cell adhesion molecule (ICAM-1), vascular cell adhesion molecule 1 (VCAM-1), hepatocyte growth factor (HGF), and monocyte chemotactic protein 1 (MCP-1) in the exposed livers were analyzed at 48 h after treatment. ALI recovery was determined by serum biochemical parameters and histology. Results The isolated rat BMSCs demonstrated a good proliferation potential that was both osteogenic and adipogenic in differentiation and expressed cluster of differentiation (CD) 29 and CD90, but not CD45 or CD11b/c. After BMSC and/or UTMD treatment, the number of GFP-labeled BMSCs in the UTMD + BMSCs group was significantly higher than that of the BMSCs group (9.8 ± 2.3 vs. 5.2 ± 1.1/per high-power field). Furthermore, the expression of GFP mRNA was performed for evaluation of the homing rate of BMSCs in injury sites as well. In addition, the expression levels of SDF-1, ICAM-1, VCAM-1, HGF, and MCP-1 were higher (p < 0.01) in UTMD+BMSCs group. The serum levels of biomarkers were significantly lower in the UTMD + BMSCs group, and the apoptotic rate of hepatocytes in the UTMD + BMSCs group was markedly lower than that of the BMSCs group (all p < 0.05). The hepatic pathology was significantly alleviated in the UTMD + BMSCs group. Conclusions UTMD treatment efficiently induced a favorable microenvironment for cell engraftment, resulting in improvement of hepatic homing of BMSCs, which was probably mediated through upregulation of the expression of adhesion molecules and cytokines. UTMD treatment appeared to be an effective and noninvasive approach to achieve better efficacy of BMSC-based therapy for repairing a severely injured liver