scDMV: a zero-one inflated beta mixture model for DNA methylation variability with scBS-seq data.

MOTIVATION: The utilization of single-cell bisulfite sequencing (scBS-seq) methods allows for precise analysis of DNA methylation patterns at the individual cell level, enabling the identification of rare populations, revealing cell-specific epigenetic changes, and improving differential methylation analysis. Nonetheless, the presence of sparse data and an overabundance of zeros and ones, attributed to limited sequencing depth and coverage, frequently results in reduced precision accuracy during the process of differential methylation detection using scBS-seq. Consequently, there is a pressing demand for an innovative differential methylation analysis approach that effectively tackles these data characteristics and enhances recognition accuracy. RESULTS: We propose a novel beta mixture approach called scDMV for analyzing methylation differences in single-cell bisulfite sequencing data, which effectively handles excess zeros and ones and accommodates low-input sequencing. Our extensive simulation studies demonstrate that the scDMV approach outperforms several alternative methods in terms of sensitivity, precision, and controlling the false positive rate. Moreover, in real data applications, we observe that scDMV exhibits higher precision and sensitivity in identifying differentially methylated regions, even with low-input samples. In addition, scDMV reveals important information for GO enrichment analysis with single-cell whole-genome sequencing data that are often overlooked by other methods. AVAILABILITY AND IMPLEMENTATION: The scDMV method, along with a comprehensive tutorial, can be accessed as an R package on the following GitHub repository: https://github.com/PLX-m/scDMV.

Selective Cleavage of α-Olefins to Produce Acetylene and Hydrogen.

Acetylene production from mixed α-olefins emerges as a potentially green and energy-efficient approach with significant scientific value in the selective cleavage of C-C bonds. On the Pd(100) surface, it is experimentally revealed that C2 to C4 α-olefins undergo selective thermal cleavage to form surface acetylene and hydrogen. The high selectivity toward acetylene is attributed to the 4-fold hollow sites which are adept at severing the terminal double bonds in α-olefins to produce acetylene. A challenge arises, however, because acetylene tends to stay at the Pd(100) surface. By using the surface alloying methodology with alien Au, the surface Pd d-band center has been successfully shifted away from the Fermi level to release surface-generated acetylene from α-olefins as a gaseous product. Our study actually provides a technological strategy to economically produce acetylene and hydrogen from α-olefins.

Correlation between oxygenation status of extra-synovial tissue of wrist and disease activity in rheumatoid arthritis: a photoacoustic imaging study.

OBJECTIVES: Rheumatoid arthritis (RA) is characterized by hypoxia in the synovial tissue. While photoacoustic imaging (PA) offers a method to evaluate tissue oxygenation in RA patients, studies exploring the link between extra-synovial tissue of wrist oxygenation and disease activity remain scarce. We aimed to assess synovial oxygenation in RA patients using a multimodal photoacoustic-ultrasound (PA/US) imaging system and establish its correlation with disease activity. METHODS: A retrospective study was conducted on 111 patients with RA and 72 healthy controls from 2022 to 2023. Dual-wavelength PA imaging quantified oxygen saturation (So2) levels in the synovial membrane and peri-wrist region. Oxygenation states were categorised as hyperoxia, intermediate oxygenation, and hypoxia based on So2 values. The association between oxygenation levels and the clinical disease activity index was evaluated using a one-way analysis of variance, complemented by the Kruskal-Wallis test with Bonferroni adjustment. RESULTS: Of the patients with RA, 39 exhibited hyperoxia, 24 had intermediate oxygenation, and 48 had hypoxia in the wrist extra-synovial tissue. All of the control participants exhibited the hyperoxia status. Oxygenation levels in patients with RA correlated with clinical metrics. Patients with intermediate oxygenation had a lower disease activity index compared with those with hypoxia and hyperoxia. CONCLUSION: A significant correlation exists between wrist extra-synovial tissue oxygenation and disease activity in patients with RA.

Characteristics and Correlations of Wall Shear Stress and Flow Turbulence in the Carotid Bifurcation Evaluated Using an Ultrasound Vector Flow Imaging.

INTRODUCTION: The aim of the study was to evaluate characteristics and provide the normal values of wall shear stress (WSS) and flow turbulence (Tur), and the relationship between them in the carotid bifurcation based on an ultrasound vector flow imaging (V Flow) in healthy adults. METHODS: Max and mean WSS and Tur values at three segments (initial segments of internal and external carotid arteries [IICA and IECA]; distal segment of common carotid artery [DCCA]), both in anterior and posterior walls, were successfully obtained in 56 healthy adults, using ultrasound V Flow function. Relationship between mean WSS and Tur was further explored. RESULTS: The mean WSS value was 0.71 Pa, 0.86 Pa, and 0.96 Pa at IICA, IECA, and DCCA, respectively (IICA < IECA < DCCA, p < 0.05). The mean Tur value was 13.85%, 5.46%, and 4.17% at IICA, IECA, and DCCA, respectively (IICA > IECA > DCCA, p < 0.05). A cutoff value (WSS = 0.4 Pa) was selected and Tur values were significantly higher in group with WSS cutoff value <0.4 Pa than group with WSS cutoff value ≥0.4 Pa (p < 0.01). CONCLUSION: WSS and Tur are moderately negatively correlated, which can be used in the quantitative evaluation of carotid bifurcation and could be a potential dual-parameter tool in the clinical research for early detection of carotid atherosclerosis.

Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.

OBJECTIVES: This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk. METHODS: We conducted a retrospective multicenter study using ultrasound images from four hospitals. Convolutional neural network (CNN) models were constructed to classify ultrasound images of thyroid nodules into solid and non-solid, as well as benign and malignant. A total of 11,201 images of 6784 nodules were used for training, validation, and testing. The area under the receiver-operating characteristic curve (AUC) was employed as the primary evaluation index. RESULTS: The models had AUCs higher than 0.91 in the benign and malignant grading of solid thyroid nodules, with the Inception-ResNet AUC being the highest at 0.94. In the test set, the best algorithm for identifying benign and malignant thyroid nodules had a sensitivity of 0.88, and a specificity of 0.86. In the human vs. DL test set, the best algorithm had a sensitivity of 0.93, and a specificity of 0.86. The Inception-ResNet model performed better than the senior physicians (p < 0.001). The sensitivity and specificity of the optimal model based on the external test set were 0.90 and 0.75, respectively. CONCLUSIONS: This research demonstrates that CNNs can assist thyroid nodule diagnosis and reduce the rate of unnecessary fine-needle aspiration (FNA). CLINICAL RELEVANCE STATEMENT: High-resolution ultrasound has led to increased detection of thyroid nodules. This results in unnecessary fine-needle aspiration and anxiety for patients whose nodules are benign. Deep learning can solve these problems to some extent. KEY POINTS: • Thyroid solid nodules have a high probability of malignancy. • Our models can improve the differentiation between benign and malignant solid thyroid nodules. • The differential performance of one model was superior to that of senior radiologists. Applying this could reduce the rate of unnecessary fine-needle aspiration of solid thyroid nodules.

Comparative study of ultrasound attenuation analysis and controlled attenuation parameter in the diagnosis and grading of liver steatosis in non-alcoholic fatty liver disease patients.

PURPOSE: To assess the diagnostic performance of Ultrasound Attenuation Analysis (USAT) in the diagnosis and grading of hepatic steatosis in patients with non-alcoholic fatty liver disease (NAFLD) using Controlled Attenuation Parameters (CAP) as a reference. MATERIALS AND METHODS: From February 13, 2023, to September 26, 2023, participants underwent CAP and USAT examinations on the same day. We used manufacturer-recommended CAP thresholds to categorize the stages of hepatic steatosis: stage 1 (mild) - 240 dB/m, stage 2 (moderate) - 265 dB/m, stage 3 (severe) - 295 dB/m. Receiver Operating Characteristic curves were employed to evaluate the diagnostic accuracy of USAT and determine the thresholds for different levels of hepatic steatosis. RESULTS: Using CAP as the reference, we observed that the average USAT value increased with the severity of hepatic steatosis, and the differences in USAT values among the different hepatic steatosis groups were statistically significant (p < 0.05). There was a strong positive correlation between USAT and CAP (r = 0.674, p < 0.0001). When using CAP as the reference, the optimal cut-off values for diagnosing and predicting different levels of hepatic steatosis with USAT were as follows: the cut-off value for excluding the presence of hepatic steatosis was 0.54 dB/cm/MHz (AUC 0.96); for mild hepatic steatosis, it was 0.59 dB/cm/MHz (AUC 0.86); for moderate hepatic steatosis, it was 0.73 dB/cm/MHz (AUC 0.81); and for severe hepatic steatosis, it was 0.87 dB/cm/MHz (AUC 0.87). CONCLUSION: USAT exhibits strong diagnostic performance for hepatic steatosis and shows a high correlation with CAP values.

The impact of maternal intrahepatic cholestasis during pregnancy on the growth trajectory of offspring: a population-based nested case‒control cohort study.

BACKGROUND: Intrahepatic cholestasis of pregnancy (ICP) is associated with an increased risk of adverse fetal outcomes, yet its influence on offspring growth remains unclear. Our study dynamically tracks growth rates in children from ICP and healthy mothers and investigates the link between maternal liver function and developmental abnormalities in offspring. METHOD: Our case‒control study involved 97 women with ICP and 152 with uncomplicated pregnancies nested in a cohort of their offspring, including 50 from the ICP group and 87 from the uncomplicated pregnancy group. We collected pediatric growth and development data, with a maximum follow-up duration of 36 months. Stratified analyses of children's height, weight, and head circumference were conducted, and Spearman's rank correlation was applied to examine the relationships between maternal serological markers and pediatric growth metrics. RESULT: Maternal liver and renal functions, along with serum lipid profiles, significantly differed between the ICP and normal groups. In the ICP group, the offspring showed elevated alanine aminotransferase (ALT), direct bilirubin (DBIT), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (APOB) levels. Notably, the length-for-age z score (LAZ), weight-for-age z score (WAZ), and head circumference-for-age z score (HCZ) were lower in ICP offspring compared with those from normal pregnancies within the 1- to 12-month age range (P < 0.05). However, no significant differences in LAZ, weight-for-length z score (WLZ), BMI-for-age z score (BAZ), or HCZ were observed between groups in the 13- to 36-month age range. Maternal maximum lactate dehydrogenase (LDH) and total bile acids (TBA) levels during pregnancy were inversely correlated with LAZ and WAZ in the first year. Furthermore, offspring of mothers with ICP exhibited a greater incidence of stunting (24% vs. 6.9%, P = 0.004) and abnormal HCZ (14% vs. 3.7%, P = 0.034). CONCLUSIONS: Growth disparities in offspring of ICP-affected pregnancies were most significant within the 1- to 12-month age range. During this period, maximum maternal LDH and TBA levels were negatively correlated with LAZ and WAZ values of offspring. The observation of similar growth rates between ICP and control group offspring from 13 to 36 months suggested catch-up growth in the ICP group.

The association between mammographic density and breast cancer risk in Chinese women: a systematic review and meta-analysis.

PURPOSE: Breast density has consistently been shown to be an independent risk factor for breast cancer in Western populations; however, few studies have evaluated this topic in Chinese women and there is not yet a unified view. This study investigated the association between mammographic density (MD) and breast cancer risk in Chinese women. METHODS: The PubMed, Cochrane Library, Embase, and Wanfang databases were systematically searched in June 2023 to include all studies on the association between MD and breast cancer risk in Chinese women. A total of 13,977 breast cancer cases from 14 studies were chosen, including 10 case-control/cross-sectional studies, and 4 case-only studies. For case-control/cross-sectional studies, the odds ratios (ORs) of MD were combined using random effects models, and for case-only studies, relative odds ratios (RORs) were combinations of premenopausal versus postmenopausal breast cancer cases. RESULTS: Women with BI-RADS density category II-IV in case-control/cross-sectional studies had a 0.93-fold (95% confidence interval [CI] 0.55, 1.57), 1.08-fold (95% CI 0.40, 2.94), and 1.24-fold (95% CI 0.42, 3.69) higher risk compared to women with the lowest density category. Combined RORs for premenopausal versus postmenopausal women in case-only studies were 3.84 (95% CI 2.92, 5.05), 22.65 (95% CI 7.21, 71.13), and 42.06 (95% CI 4.22, 419.52), respectively, for BI-RADS density category II-IV versus I. CONCLUSIONS: For Chinese women, breast cancer risk is weakly associated with MD; however, breast cancer risk is more strongly correlated with mammographic density in premenopausal women than postmenopausal women. Further research on the factors influencing MD in premenopausal women may provide meaningful insights into breast cancer prevention in China.

Development and validation of nomograms using photoacoustic imaging and 2D ultrasound to predict breast nodule benignity and malignancy.

BACKGROUND: The application of photoacoustic imaging (PAI), utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. However, its effectiveness in distinguishing between malignant and benign nodules remains insufficiently explored. PURPOSE: This study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI. METHOD: A prospective cohort study enrolled 369 breast nodules, subjecting them to PAI and ultrasound examination. The training and testing cohorts were randomly divided into two cohorts in a ratio of 3:1. Based on the source of the variables, three models were developed, Model 1: photoacoustic-BIRADS+BMI + blood oxygenation, Model 2: BIRADS+Shape+Intranodal blood (Doppler) + BMI, Model 3: photoacoustic-BIRADS+BIRADS+ Shape+Intranodal blood (Doppler) + BMI + blood oxygenation. Risk factors were identified through logistic regression, resulting in the creation of three predictive models. These models were evaluated using calibration curves, subject receiver operating characteristic (ROC), and decision curve analysis. RESULTS: The area under the ROC curve for the training cohort was 0.91 (95% confidence interval, 95% CI: 0.88-0.95), 0.92 (95% CI: 0.89-0.95), and 0.97 (95% CI: 0.96-0.99) for Models 1-3, and the ROC curve for the testing cohort was 0.95 (95% CI: 0.91-0.98), 0.89 (95% CI: 0.83-0.96), and 0.97 (95% CI: 0.95-0.99) for Models 1-3. CONCLUSIONS: The calibration curves demonstrate that the model's predictions agree with the actual values. Decision curve analysis suggests a good clinical application.

An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer.

PURPOSE: We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. METHODS: A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. RESULTS: In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). CONCLUSION: The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.

A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images.

OBJECTIVES: Assessment of plaque stenosis severity allows better management of carotid source of stroke. Our objective is to create a deep learning (DL) model to segment carotid intima-media thickness and plaque and further automatically calculate plaque stenosis severity on common carotid artery (CCA) transverse section ultrasound images. METHODS: Three hundred and ninety images from 376 individuals were used to train (235/390, 60%), validate (39/390, 10%), and test (116/390, 30%) on a newly proposed CANet model. We also evaluated the model on an external test set of 115 individuals with 122 images acquired from another hospital. Comparative studies were conducted between our CANet model with four state-of-the-art DL models and two experienced sonographers to re-evaluate the present model's performance. RESULTS: On the internal test set, our CANet model outperformed the four comparative models with Dice values of 95.22% versus 90.15%, 87.48%, 90.22%, and 91.56% on lumen-intima (LI) borders and 96.27% versus 91.40%, 88.94%, 91.19%, and 92.88% on media-adventitia (MA) borders. On the external test set, our model still produced excellent results with a Dice value of 92.41%. Good consistency of stenosis severity calculation was observed between CANet model and experienced sonographers, with Intraclass Correlation Coefficient (ICC) of 0.927 and 0.702, Pearson's Correlation Coefficient of 0.928 and 0.704 on internal and external test set, respectively. CONCLUSIONS: Our CANet model achieved excellent performance in the segmentation of carotid IMT and plaques as well as automated calculation of stenosis severity.

A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors.

BACKGROUND: The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However, the impact of US-probe frequencies on the diagnostic efficacy of AI models has not been clearly established. OBJECTIVES: To explore the impact of using US-video of variable frequencies on the diagnostic efficacy of AI in breast US screening. METHODS: This study utilized different frequency US-probes (L14: frequency range: 3.0-14.0 MHz, central frequency 9 MHz, L9: frequency range: 2.5-9.0 MHz, central frequency 6.5 MHz and L13: frequency range: 3.6-13.5 MHz, central frequency 8 MHz, L7: frequency range: 3-7 MHz, central frequency 4.0 MHz, linear arrays) to collect breast-video and applied an entropy-based deep learning approach for evaluation. We analyzed the average two-dimensional image entropy (2-DIE) of these videos and the performance of AI models in processing videos from these different frequencies to assess how probe frequency affects AI diagnostic performance. RESULTS: The study found that in testing set 1, L9 was higher than L14 in average 2-DIE; in testing set 2, L13 was higher in average 2-DIE than L7. The diagnostic efficacy of US-data, utilized in AI model analysis, varied across different frequencies (AUC: L9 > L14: 0.849 vs. 0.784; L13 > L7: 0.920 vs. 0.887). CONCLUSION: This study indicate that US-data acquired using probes with varying frequencies exhibit diverse average 2-DIE values, and datasets characterized by higher average 2-DIE demonstrate enhanced diagnostic outcomes in AI-driven BCa diagnosis. Unlike other studies, our research emphasizes the importance of US-probe frequency selection on AI model diagnostic performance, rather than focusing solely on the AI algorithms themselves. These insights offer a new perspective for early BCa screening and diagnosis and are of significant for future choices of US equipment and optimization of AI algorithms.

The research on artificial intelligence-assisted breast diagnosis often relies on static images or dynamic videos obtained from ultrasound probes with different frequencies. However, the effect of frequency-induced image variations on the diagnostic performance of artificial intelligence models remains unclear. In this study, we aimed to explore the impact of using ultrasound images with variable frequencies on AI's diagnostic efficacy in breast ultrasound screening. Our approach involved employing a video and entropy-based feature breast network to compare the diagnostic efficiency and average two-dimensional image entropy of the L14 (frequency range: 3.0-14.0 MHz, central frequency 9 MHz), L9 (frequency range: 2.5-9.0 MHz, central frequency 6.5 MHz) linear array probe and L13 (frequency range: 3.6-13.5 MHz, central frequency 8 MHz), and L7 (frequency range: 3-7 MHz, central frequency 4.0 MHz) linear array probes. The results revealed that the diagnostic efficiency of AI models differed based on the frequency of the ultrasound probe. It is noteworthy that ultrasound images acquired with different frequency probes exhibit different average two-dimensional image entropy, while higher average two-dimensional image entropy positively affect the diagnostic performance of the AI model. We concluded that a dataset with higher average two-dimensional image entropy is associated with superior diagnostic efficacy for AI-based breast diagnosis. These findings contribute to a better understanding of how ultrasound image variations impact AI-assisted breast diagnosis, potentially leading to improved breast cancer screening outcomes.

Development and Application of Unmanned Aerial High-Resolution Convex Grating Dispersion Hyperspectral Imager.

This study presents the design and development of a high-resolution convex grating dispersion hyperspectral imaging system tailored for unmanned aerial vehicle (UAV) remote sensing applications. The system operates within a spectral range of 400 to 1000 nm, encompassing over 150 channels, and achieves an average spectral resolution of less than 4 nm. It features a field of view of 30°, a focal length of 20 mm, a compact volume of only 200 mm × 167 mm × 78 mm, and a total weight of less than 1.5 kg. Based on the design specifications, the system was meticulously adjusted, calibrated, and tested. Additionally, custom software for the hyperspectral system was independently developed to facilitate functions such as control parameter adjustments, real-time display, and data preprocessing of the hyperspectral camera. Subsequently, the prototype was integrated onto a drone for remote sensing observations of Spartina alterniflora at Yangkou Beach in Shouguang City, Shandong Province. Various algorithms were employed for data classification and comparison, with support vector machine (SVM) and neural network algorithms demonstrating superior classification accuracy. The experimental results indicate that the UAV-based hyperspectral imaging system exhibits high imaging quality, minimal distortion, excellent resolution, an expansive camera field of view, a broad detection range, high experimental efficiency, and remarkable capabilities for remote sensing detection.

Selective Activation of Lattice Oxygen Site Through Coordination Engineering to Boost the Activity and Stability of Oxygen Evolution Reaction.

Although Ru-based materials are among the outstanding catalysts for the oxygen evolution reaction (OER), the instability issue still haunts them and impedes the widespread application. The instability of Ru-based OER catalysts is generally ascribed to the formation of soluble species through the over-oxidation of Ru and structural decomposition caused by involvement of lattice oxygen. Herein, an effective strategy of selectively activating the lattice oxygen around Ru site is proposed to improve the OER activity and stability. Our synthesized spinel-type electrocatalyst of Ru and Zn co-doped Co3O4 showed an ultralow overpotential of 172 mV at 10 mA cm-2 and a long-term stability reaching to 100 hours at 10 mA cm-2 for alkaline OER. The experimental results and theoretical simulations demonstrated that the lattice oxygen site jointly connected with the octahedral Ru and tetrahedral Zn atoms became more active than other oxygen sites near Ru atom, which further lowered the reaction energy barriers and avoided generating excessive oxygen vacancies to enhance the structural stability of Ru sites. The findings hope to provide a new perspective to improve the catalytic activity of Ru-incorporated OER catalysts and the stability of lattice-oxygen-mediated mechanism.

SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling.

Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer's Disease Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10 000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https://github.com/Myuan2019/SCEBE.

Feasibility and reproducibility of semi-automated longitudinal strain analysis: a comparative study with conventional manual strain analysis.

BACKGROUND: Conventional approach to myocardial strain analysis relies on a software designed for the left ventricle (LV) which is complex and time-consuming and is not specific for right ventricular (RV) and left atrial (LA) assessment. This study compared this conventional manual approach to strain evaluation with a novel semi-automatic analysis of myocardial strain, which is also chamber-specific. METHODS: Two experienced observers used the AutoStrain software and manual QLab analysis to measure the LV, RV and LA strains in 152 healthy volunteers. Fifty cases were randomly selected for timing evaluation. RESULTS: No significant differences in LV global longitudinal strain (LVGLS) were observed between the two methods (-21.0% ± 2.5% vs. -20.8% ± 2.4%, p = 0.230). Conversely, RV longitudinal free wall strain (RVFWS) and LA longitudinal strain during the reservoir phase (LASr) measured by the semi-automatic software differed from the manual analysis (RVFWS: -26.4% ± 4.8% vs. -31.3% ± 5.8%, p < 0.001; LAS: 48.0% ± 10.0% vs. 37.6% ± 9.9%, p < 0.001). Bland-Altman analysis showed a mean error of 0.1%, 4.9%, and 10.5% for LVGLS, RVFWS, and LASr, respectively, with limits of agreement of -2.9,2.6%, -8.1,17.9%, and -12.3,33.3%, respectively. The semi-automatic method had a significantly shorter strain analysis time compared with the manual method. CONCLUSIONS: The novel semi-automatic strain analysis has the potential to improve efficiency in measurement of longitudinal myocardial strain. It shows good agreement with manual analysis for LV strain measurement.

Design of a real-time orientation sensor based on atmospheric polarization light.

In recent years, biomimetic polarization navigation has become a research hotspot in navigation fields because of its autonomy and concealment. Existing point-source polarization navigation sensors mainly use a logarithmic amplifier as the arithmetic unit to obtain polarization information. However, these sensors suffer from zero drift and low detection accuracy, which limits their application range. To address the above issues, a polarization navigation sensor based on a differential amplifier is designed as the operational unit. Based on the change of the arithmetic unit of the polarization signal, the algorithm for calculating the heading angle of the sensor is improved. The results of the orientation experiments with the designed sensor in clear weather indicate that the orientation error is ±1.243∘, and the standard deviation is 0.351°. The polarization navigation sensor can extract polarized light information and calculate the heading without accumulation of errors over time accurately and achieves good real-time performance.

Blood Flow Turbulence Quantification of Carotid Artery With a High-Frame Rate Vector Flow Imaging.

OBJECTIVES: To assess the feasibility and performance of Turbulence (Tur) index as a quantitative tool for carotid artery flow turbulence; to detect and compare the blood flow patterns of common carotid artery (CCA) and carotid bulb (CB) at different ages and cardiac phases in healthy adults, and thus interpret the evolvement of etiology difference between CCA and CB. METHODS: Carotid flow characteristics of 40 healthy volunteers were evaluated quantitatively by a high-frame rate vector flow imaging. Three types of flow patterns were defined depending on the distributive range of complex flow during systole in CB. Comparison of mean Tur value in CCA and CB at different age groups and cardiac phases was performed. And the correlation between Tur value and the diameter ratio of proximal internal carotid artery to common carotid artery (DRpro-ica/cca) was tested. RESULTS: Mean Tur values in CB were remarkably higher than that in CCA, whether during systole or diastole (P < .001). Meanwhile Tur values in CB during systole were significantly higher than that during diastole (P < .001). Flow complexity of CB showed variations among 40 participants especially in systole, whereas the flow pattern of CCA was relatively consistent. Mean Tur values were positively correlated with DRpro-ica/cca in CB (ρ = 0.69, P < .05). CONCLUSIONS: V Flow imaging provided a reliable method-Tur, for quantitative analysis of carotid blood flow. It had potential to be further applied in distinguishing complex hemodynamic characteristics in high-risk people of carotid diseases for the risk stratification of cardiovascular events.

The spatial expression of mTORC2-AKT-IP3R signal pathway in mitochondrial combination of endoplasmic reticulum of maternal fetal interface trophoblast in intrahepatic cholestasis of pregnancy.

OBJECTIVES: Intrahepatic cholestasis of pregnancy (ICP) is complicated by adverse fetal outcomes and even fetal death, the mechanism remains unclear. This study aims at evaluating the differential expression of mTORC2-AKT-IP3R signaling pathway, which accurately regulate Ca2+ transfer across mitochondria-associated membranes (MAMs) and determine the stress intensity experienced by endoplasmic reticulum (ER) and mitochondria, in patients diagnosed with ICP. METHODS: We combined western blot analysis and placental immunofluorescence co-localization detection to assess the expression and co-localization of the mTORC2-AKT-IP3R signaling pathway in severe (maternal total bile acid (TBA) levels ≥40 µmol/L) and mild (maternal TBA 10-40 µmol/L) ICP. RESULTS: Compared with the control and mild ICP groups, phosphorylated protein kinase B (p-AKT) levels were significantly upregulated in the severe ICP group. Placental Rictor levels were lower in the mild ICP group than in the control group and were further downregulated in the severe ICP group. IP3R3 and p-IP3R3 levels were lower in placentas in the severe ICP group than in those in the mild ICP and control groups. Moreover, the co-localization of IP3R3 and p-AKT in patients in the mild and severe ICP groups was significantly elevated compared with that in patients in the control group. CONCLUSIONS: In patients with severe ICP, limited expression of Rictor and elevated p-AKT levels would suppress IP3R3/p-IP3R3 levels in MAMs. This inhibition might influence the transportation of Ca2+ from the ER to the mitochondria, thus weaken the stress adaptation associated with MAMs. Our results reveal the possible pathophysiological mechanism of adverse fetal outcomes in ICP.

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study.

BACKGROUND: This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images. METHODS: A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed. RESULTS: A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively. CONCLUSIONS: This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.