Apical aneurysm formation in apical hypertrophic cardiomyopathy: Pilot study with cardiac magnetic resonance

BACKGROUND

The dynamic changes and apical aneurysm formation in apical hypertrophic cardiomyopathy (HCM) have not been specifically described. This study aimed to describe these changes to better understand the progression of apical HCM.

METHODS AND RESULTS

Seventy-two patients with apical HCM who underwent at least two cardiac magnetic resonance (CMR) examinations were retrospectively included in this study. The mean interval between the first and last CMR examinations was 50.1 ± 26.8 months (ranging from 4 to 118 months). Compared with the initial values, the left atrial diameter, maximum left ventricular wall thickness and late gadolinium enhancement extent significantly increased (all P < 0.05), while the left ventricular ejection fraction significantly decreased (P < 0.05), at the latest CMR examination. More importantly, the dynamic process of apical aneurysm formation in apical HCM was observed in a subset of patients, which may follow these four stages: starting with systolic apical cavity obliteration, then broadening of the apical slit in systole, further developing into an apical outpouching, and finally forming an apical aneurysm. Eleven patients experienced adverse cardiovascular events, including new-onset or progressive atrial fibrillation (n = 7), hospitalization with heart failure (n = 3) and implantable cardioverter defibrillator intervention (n = 1), at the time of the latest CMR examination.

CONCLUSIONS

In the progression of apical HCM, cardiac structure and function will change accordingly. Apical aneurysm formation in apical HCM is a chronic and continuous dynamic process that may follow a 4-step pathway of disease progression.

Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis

Background

With the increasing interest of academics in the application of artificial intelligence to sepsis, thousands of papers on this field had been published in the past few decades. It is difficult for researchers to understand the themes and latest research frontiers in this field from a multi-dimensional perspective. Consequently, the purpose of this study is to analyze the relevant literature in the application of artificial intelligence to sepsis through bibliometrics software, so as to better understand the development status, study the core hotspots and future development trends of this field.

Methods

We collected relevant publications in the application of artificial intelligence to sepsis from the Web of Science Core Collection in 2000 to 2021. The type of publication was limited to articles and reviews, and language was limited to English. Research cooperation network, journals, cited references, keywords in this field were visually analyzed by using CiteSpace, VOSviewer, and COOC software.

Results

A total of 8,481 publications in the application of artificial intelligence to sepsis between 2000 and 2021 were included, involving 8,132 articles and 349 reviews. Over the past 22 years, the annual number of publications had gradually increased exponentially. The USA was the most productive country, followed by China. Harvard University, Schuetz, Philipp, and Intensive Care Medicine were the most productive institution, author, and journal, respectively. Vincent, Jl and Critical Care Medicine were the most cited author and cited journal, respectively. Several conclusions can be drawn from the analysis of the cited references, including the following: screening and identification of sepsis biomarkers, treatment and related complications of sepsis, and precise treatment of sepsis. Moreover, there were a spike in searches relating to machine learning, antibiotic resistance and accuracy based on burst detection analysis.

Conclusion

This study conducted a comprehensive and objective analysis of the publications on the application of artificial intelligence in sepsis. It can be predicted that precise treatment of sepsis through machine learning technology is still research hotspot in this field.

The Influence of Cryogenic Treatment on the Microstructure and Mechanical Characteristics of Aluminum Silicon Carbide Matrix Composites

Aluminum matrix composites have been widely used in aerospace and automotive fields due to their excellent physical properties. Cryogenic treatment was successfully adopted to improve the performance of aluminum alloy components, while its effect and mechanism on the aluminum matrix composite remained unclear. In this work, the effects of cryogenic treatment on the microstructure evolution and mechanical properties of 15%SiCp/2009 aluminum matrix composites were systematically investigated by means of Thermoelectric Power (TEP), Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The results showed that TEP measurement can be an effective method for evaluating the precipitation characteristics of 15%SiCp/2009 aluminum matrix composites during aging. The addition of cryogenic treatment after solution and before aging treatment promoted the precipitation from the beginning stage of aging. Furthermore, the aging time for the maximum precipitation of the θ″ phase was about 4 h advanced, as the conduction of cryogenic treatment accelerates the aging kinetics. This was attributed to the great difference in the linear expansion coefficient between the aluminum alloy matrix and SiC-reinforced particles, which could induce high internal stress in their boundaries for precipitation. Moreover, the lattice contraction of the aluminum alloy matrix during cryogenic treatment led to the increase in dislocation density and micro defects near the boundaries, thus providing more nucleation sites for precipitation during the aging treatment. After undergoing artificial aging treatment for 20 h, the increase in dispersive, distributed precipitates after cryogenic treatment improved the hardness and yield strength by 4% and 16 MPa, respectively.

A left pulmonary artery sling with left bronchiectasis in an adult patient: A case report and review of literature

Pulmonary artery sling (PAS) is a rare congenital vascular anomaly, and is usually diagnosed during the infantile or fetal period. Adult presentation of PAS is rare. We report a 55-year-old woman with left pulmonary artery sling and left lung bronchiectasis, performing as persistent shortness of breath, coronary computed tomography angiography (CTA) showed the aberrant left pulmonary artery emerging from the right pulmonary artery and crossing to the left between the trachea and oesophagus. We experienced a rare adult case with LPAS and left bronchiectasis, stressing the importance of the anatomic abnormalities in such cases.

Gender Disparities in Academic Pain Medicine: A Retrospective, Cross-Sectional Bibliometric Analysis

PURPOSE

This study was conducted to characterize the gender disparities within academic pain management departments in the United States, specifically focusing on its relation to research and academic leadership. This will allow for targeted improvements in efforts made to reduce gender gaps within academic pain medicine.

METHODS

This is a retrospective, cross-sectional analysis study evaluating pain management faculty of various positions at academic institutions across the United States. We utilized publicly available data on faculty positions and sex to analyze research impact, H-index, number of publications and citations through bibliometric and linear regression analysis.

RESULTS

Our analysis found that female faculty had significantly less research output to male faculty. The three research measurement indices used in this study including H-index, number of publications, and number of citations were significantly lower in females than in males among associate and full professor faculty ranking. Multivariable analysis did not display any significant disparities of research output at the division director and department chair level.

DISCUSSION

As in many areas of medicine, there continues to be a significant gender disparity in academic pain management departments, particularly with regard to leadership positions and research impact within the field. Our study found that female pain physicians had a significantly less research output based on the three variables of H indices, number of publications, and number of citations compared to their male counterparts. This has been shown to have the impact on discrepancies in female faculty ranking. Interestingly, these variables were not significantly different between male and female faculty members of the same level of leadership except for program director. There are various contributory reasons for these disparities, including implicit biases, lack of mentorship, and familial obligations. Addressing some of these factors can help narrow the schism and promote greater gender equality within academic pain management.

An "off-on" electrochemiluminescence aptasensor for determination of lincomycin based on CdS QDs/carboxylated g-C3N4

A novel electrochemiluminescence (ECL) aptasensor for the determination of lincomycin (LIN) was developed based on CdS QDs/carboxylated g-C₃N₄ (CdS QDs/C-g-C₃N₄). CdS QDs/C-g-C₃N₄ served as the substrate of the aptasensor, and then CdS QDs/C-g-C₃N₄-modified electrode was incubated with aptamer DNA (Apt-DNA). When the non-specific sites of the electrode surface was blocked by 6-mercaptohexanol, the ferrocene-labeled probe (Fer-DNA) was assembled onto the electrode surface through base complementation with Apt-DNA. In the absence of LIN, the ECL signal was quenched effectively by Fer-DNA and a decreased ECL emission (off state) was acquired. On the contrary, LIN was specifically bond with Apt-DNA, and Fer-DNA was detached from the aptasensor surface because of the deformation of Apt-DNA, resulting in an effectively enhanced ECL signal (on state). The constructed ECL aptasensor exhibited a wide detection range for LIN determination (0.05 ng mL^-1-100 μg mL^-1) with a low detection limit (0.02 ng mL^-1). Importantly, the proposed ECL aptasensor showed outstanding accuracy and specificity for LIN determination, and also provided a potential strategy for other antibiotic determinations.

Analysis of a machine learning-based risk stratification scheme for acute kidney injury in vancomycin

Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.

Protective Actions in Apical Periodontitis: The Regenerative Bioactivities Led by Mesenchymal Stem Cells

Resulting from bacterial infection, apical periodontitis (AP) is a common inflammatory disease of the periapical region of the tooth. The regeneration of the destroyed periapical alveolar bone and the surrounding periodontium tissues has long been a difficult task in clinical practice. These lesions are closely related to pathogen invasion and an overreactive immune response. It is worth noting that the protective healing process occurs simultaneously, in which mesenchymal stem cells (MSCs) have a crucial function in mediating the immune system and promoting regeneration. Here, we review the recent studies related to AP, with a focus on the regulatory network of MSCs. We also discuss the potential therapeutic approaches of MSCs in inflammatory diseases to provide a basis for promoting tissue regeneration and modulating inflammation in AP. A deeper understanding of the protective action of MSCs and the regulatory networks will help to delineate the underlying mechanisms of AP and pave the way for stem-cell-based regenerative medicine in the future.

Assessing public health and economic loss associated with black carbon exposure using monitoring and MERRA-2 data

Black carbon (BC) exposure in China continues to be relatively high, prompting researchers to assess BC exposure levels using data from monitoring sites, satellite remote sensing, and models. However, data regarding the application of a combined strategy comprising the analysis of monitoring data and various types of data to simulate BC exposure levels are lacking. Hence, the current study seeks to estimate short- and long-term BC exposure levels by combining national monitoring data with data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Furthermore, this study attempts to improve the spatio-temporal resolution of BC exposure levels using Bayesian maximum entropy (BME). The BME model performed well in terms of estimating short- (R² = 0.74 and RMSE = 1.76 μg/m³) and long-term (R² = 0.76 and RMSE = 1.3 μg/m³) exposure. Premature mortalities and economic losses were also assessed by applying localised concentration-response coefficients simulated in China. A total of 74,500 (95% confidence interval (CI): 23,900-124,500) and 538,400 (95% CI: 495,000-581,300) all-cause premature mortality cases were found to be associated with short- and long-term BC exposure, respectively. Meanwhile, short-term BC exposure was associated with economic losses ranging from 7.5 to 13.2 billion US dollars (USD) (1 USD = 6.36 RMB on January 19, 2022) based on amended human capital (AHC) and willingness to pay (WTP), accounting for 0.06%-0.1% of China's total gross domestic product (GDP) in 2017 (1.2 × 10⁴ billion USD), respectively. The economic losses for long-term exposure varied from 53 to 93.2 billion USD based on AHC and WTP, accounting for 0.4%-0.8% of China's total GDP in 2017, respectively.

How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10-14 in ultrasound using deep learning

PURPOSE

To investigate if artificial intelligence can identify fetus intracranial structures in pregnancy week 11-14; to provide an automated method of standard and non-standard sagittal view classification in obstetric ultrasound examination METHOD AND MATERIALS: We proposed a newly designed scheme based on deep learning (DL) - Fetus Framework to identify nine fetus intracranial structures: thalami, midbrain, palate, 4th ventricle, cisterna magna, nuchal translucency (NT), nasal tip, nasal skin, and nasal bone. Fetus Framework was trained and tested on a dataset of 1528 2D sagittal-view ultrasound images from 1519 females collected from Shenzhen People's Hospital. Results from Fetus Framework were further used for standard/non-standard (S-NS) plane classification, a key step for NT measurement and Down Syndrome assessment. S-NS classification was also tested with 156 images from the Longhua branch of Shenzhen People's Hospital. Sensitivity, specificity, and area under the curve (AUC) were evaluated for comparison among Fetus Framework, three classic DL models, and human experts with 1-, 3- and 5-year ultrasound training. Furthermore, 4 physicians with more than 5 years of experience conducted a reader study of diagnosing fetal malformation on a dataset of 316 standard images confirmed by the Fetus framework and another dataset of 316 standard images selected by physicians. Accuracy, sensitivity, specificity, precision, and F1-Score of physicians' diagnosis on both sets are compared.

RESULTS

Nine intracranial structures identified by Fetus Framework in validation are all consistent with that of senior radiologists. For S-NS sagittal view identification, Fetus Framework achieved an AUC of 0.996 (95%CI: 0.987, 1.000) in internal test, at par with classic DL models. In external test, FF reaches an AUC of 0.974 (95%CI: 0.952, 0.995), while ResNet-50 arrives at AUC∼0.883, 95% CI 0.828-0.939, Xception AUC∼0.890, 95% CI 0.834-0.946, and DenseNet-121 AUC∼0.894, 95% CI 0.839-0.949. For the internal test set, the sensitivity and specificity of the proposed framework are (0.905, 1), while the first-, third-, and fifth-year clinicians are (0.619, 0.986), (0.690, 0.958), and (0.798, 0.986), respectively. For the external test set, the sensitivity and specificity of FF is (0.989, 0.797), and first-, third-, and fifth-year clinicians are (0.533, 0.875), (0.609, 0.844), and (0.663, 0.781), respectively.On the fetal malformation classification task, all physicians achieved higher accuracy and F1-Score on Fetus selected standard images with statistical significance (p < 0.01).

CONCLUSION

We proposed a new deep learning-based Fetus Framework for identifying key fetus intracranial structures. The framework was tested on data from two different medical centers. The results show consistency and improvement from classic models and human experts in standard and non-standard sagittal view classification during pregnancy week 11-13+6.

CLINICAL RELEVANCE/APPLICATION

With further refinement in larger population, the proposed model can improve the efficiency and accuracy of early pregnancy test using ultrasound examination.

Preoperative Cervical Cobb Angle Is a Risk Factor for Postoperative Axial Neck Pain after Anterior Cervical Discectomy and Fusion with Zero-Profile Interbody

OBJECTIVES

Anterior cervical discectomy and fusion (ACDF) with zero-profile interbody has a lower incidence of complications in treating cervical spondylotic myelopathy (CSM). However, postoperative axial neck pain is still commonly occurred, and the factors affecting which is not known. Here, we retrospectively analyze the risk factors for postoperative axial pain after performing ACDF with zero-profile implant in single-level CSM.

METHODS

Patients who suffered from single-level CSM and who received ACDF with zero-profile implant between 2018 January to 2020 December were reviewed. Of 180 single-level CSM patients, 144 patients who passed the inclusion criteria were enrolled. Patients were divided into two groups according to the severity of postoperative axial pain as measured by postoperative neck visual analogue scale (nVAS). Clinical parameters including age, sex, smoking history, symptom duration, body mass index (BMI), the Japanese Orthopaedic Association (JOA) scores, as well as radiological parameters were obtained pre- and post-operatively, and the data were compared between two groups. Pearson's chi-square tests and Mann-Whitney U tests were implemented to identify statistically significant differences between subgroups for categorical and continuous data, respectively; otherwise, the data were tested with Student's t-test. Risk factors were identified using logistic regression.

RESULTS

Of the patients (97.8%) achieved satisfied neurological recovery, and 88.2% of the patients achieved fusion at 1-year follow-up. 33% of the patients (48 patients out of 144) had sustained postoperative axial pain after the surgery. Comparison of different severity groups exhibited no significant differences in terms of the possible risk factors (P > 0.05) except for pre- and post-operative C2-C7 Cobb angles (6.33 ± 6.53 vs. 11.88 ± 7.41, P < 0.05; 13.49 ± 5.31 vs 16.64 ± 7.34, P < 0.05). Furthermore, correlation analysis showed that the preoperative C2-C7 Cobb angle is significantly correlated with the severity of the postoperative axial pain (R²  = 0.83, P < 0.01). In addition, logistic regression analysis demonstrated that the preoperative C2-C7 Cobb angle is an independent predictor of postoperative axial pain (P < 0.01, OR = 0.53). Further receiver operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.78 (P < 0.01) for preoperative C2-C7 Cobb angle, and the optimal cutoff was 8.4° (sensitivity 0.77, specificity 0.65).

CONCLUSION

The pre-operative C2-C7 Cobb angle is a risk factor for severe postoperative axial pain after anterior cervical discectomy and fusion with zero-profile interbody, and we should be cautious when poor preoperative C2-C7 Cobb angle is found in myelopathy patients planning to use zero-profile interbody to treat such patients.

Sensitive, Selective and Reliable Detection of Fe3+ in Lake Water via Carbon Dots-Based Fluorescence Assay

In this study, C-dots were facilely synthesized via microwave irradiation using citric acid and ethylenediamine as carbon precursors. The fluorescence emissions of the C-dots could be selectively quenched by Fe³⁺, and the degree of quenching was linearly related to the concentrations of Fe³⁺ presented. This phenomenon was utilized to develop a sensitive fluorescence assay for Fe³⁺ detection with broad linear range (0–250, 250–1200 μmol/L) and low detection limit (1.68 μmol/L). Most importantly, the assay demonstrated high reliability towards samples in deionized water, tap water and lake water, which should find potential applications for Fe³⁺ monitoring in complicated environments.

Exosomes derived from mesenchymal stem cells attenuate diabetic kidney disease by inhibiting cell apoptosis and epithelial-to-mesenchymal transition via miR-424-5p

Diabetic kidney disease (DKD) is well-acknowledged as one of the most common complications in diabetes mellitus. Recent studies have demonstrated the promising role of mesenchymal stem cell-derived exosomes (MSC-exos) as a cell-free treatment strategy for DKD. The present study sought to investigate the therapeutic potential and the underlying mechanisms of MSC-exos in DKD. The authentication of MSC-exos was validated by western blot, transmission electron microscope (TEM), and nanosight tracking analysis (NTA). Apoptosis was detected by western blot, TUNEL staining, and flow cytometry. Epithelial-to-mesenchymal transition (EMT) was evaluated by western blot and immunofluorescence. The relationship between miR-424-5p and Yes-associated protein 1 (YAP1) was revealed by dual luciferase reporter assay. We observed that MSC-exos could attenuate DKD by decreasing cell apoptosis and inhibiting epithelial-to-mesenchymal transition (EMT) in diabetic kidneys in db/db mice. Besides, we documented that MSC-exos could reverse high glucose-induced apoptosis and EMT in HK2 cells. Interestingly, miR-424-5p derived from MSC-exos could inhibit YAP1 activation in HK2 cells, resulting in alleviation of high glucose-induced cell apoptosis and EMT. Our study provides novel insights into MSC-exos-mediated protective effect in DKD. MSC-exos could inhibit high glucose-induced apoptosis and EMT through miR-424-5p targeting of YAP1.

Mechanically robust bamboo node and its hierarchically fibrous structural design

Although short bamboo nodes function in mechanical support and fluid exchange for bamboo survival, their structures are not fully understood compared to unidirectional fibrous internodes. Here, we identify the spatial heterostructure of the bamboo node via multiscale imaging strategies and investigate its mechanical properties by multimodal mechanical tests. We find three kinds of hierarchical fiber reinforcement schemes that originate from the bamboo node, including spatially tightened interlocking, triaxial interconnected scaffolding and isotropic intertwining. These reinforcement schemes, built on porous vascular bundles, microfibers and more-refined twist-aligned nanofibers, govern the structural stability of the bamboo via hierarchical toughening. In addition, the spatial liquid transport associated with these multiscale fibers within the bamboo node is experimentally verified, which gives perceptible evidence for life-indispensable multidirectional fluid exchange. The functional integration of mechanical reinforcement and liquid transport reflects the fact that the bamboo node has opted for elaborate structural optimization rather than ingredient richness. This study will advance our understanding of biological materials and provide insight into the design of fiber-reinforced structures and biomass utilization.

A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa

Pseudomonas aeruginosa is a ubiquitous opportunistic bacterial pathogen, which is a leading cause of nosocomial pneumonia. Early identification of the risk factors is urgently needed for severe infection patients with P. aeruginosa. However, no detailed relevant investigation based on machine learning has been reported, and little research has focused on exploring relationships between key risk clinical variables and clinical outcome of patients. In this study, we collected 571 severe infections with P. aeruginosa patients admitted to the Xijing Hospital of the Fourth Military Medical University from January 2010 to July 2021. Basic clinical information, clinical signs and symptoms, laboratory indicators, bacterial culture, and drug related were recorded. Machine learning algorithm of XGBoost was applied to build a model for predicting mortality risk of P. aeruginosa infection in severe patients. The performance of XGBoost model (AUROC = 0.94 ± 0.01, AUPRC = 0.94 ± 0.03) was greater than the performance of support vector machine (AUROC = 0.90 ± 0.03, AUPRC = 0.91 ± 0.02) and random forest (AUROC = 0.93 ± 0.03, AUPRC = 0.89 ± 0.04). This study also aimed to interpret the model and to explore the impact of clinical variables. The interpretation analysis highlighted the effects of age, high-alert drugs, and the number of drug varieties. Further stratification clarified the necessity of different treatment for severe infection for different populations.

PTHrP promotes subchondral bone formation in TMJ-OA

PTH-related peptide (PTHrP) improves the bone marrow micro-environment to activate the bone-remodelling, but the coordinated regulation of PTHrP and transforming growth factor-β (TGFβ) signalling in TMJ-OA remains incompletely understood. We used disordered occlusion to establish model animals that recapitulate the ordinary clinical aetiology of TMJ-OA. Immunohistochemical and histological analyses revealed condylar fibrocartilage degeneration in model animals following disordered occlusion. TMJ-OA model animals administered intermittent PTHrP (iPTH) exhibited significantly decreased condylar cartilage degeneration. Micro-CT, histomorphometry, and Western Blot analyses disclosed that iPTH promoted subchondral bone formation in the TMJ-OA model animals. In addition, iPTH increased the number of osterix (OSX)-positive cells and osteocalcin (OCN)-positive cells in the subchondral bone marrow cavity. However, the number of osteoclasts was also increased by iPTH, indicating that subchondral bone volume increase was mainly due to the iPTH-mediated increase in the bone-formation ability of condylar subchondral bone. In vitro, PTHrP treatment increased condylar subchondral bone marrow-derived mesenchymal stem cell (SMSC) osteoblastic differentiation potential and upregulated the gene and protein expression of key regulators of osteogenesis. Furthermore, we found that PTHrP-PTH1R signalling inhibits TGFβ signalling during osteoblastic differentiation. Collectively, these data suggested that iPTH improves OA lesions by enhancing osteoblastic differentiation in subchondral bone and suppressing aberrant active TGFβ signalling. These findings indicated that PTHrP, which targets the TGFβ signalling pathway, may be an effective biological reagent to prevent and treat TMJ-OA in the clinic.

A Comparative Study of Multiple Deep Learning Models Based on Multi-Input Resolution for Breast Ultrasound Images

Purpose

The purpose of this study was to explore the performance of different parameter combinations of deep learning (DL) models (Xception, DenseNet121, MobileNet, ResNet50 and EfficientNetB0) and input image resolutions (REZs) (224 × 224, 320 × 320 and 488 × 488 pixels) for breast cancer diagnosis.

Methods

This multicenter study retrospectively studied gray-scale ultrasound breast images enrolled from two Chinese hospitals. The data are divided into training, validation, internal testing and external testing set. Three-hundreds images were randomly selected for the physician-AI comparison. The Wilcoxon test was used to compare the diagnose error of physicians and models under P=0.05 and 0.10 significance level. The specificity, sensitivity, accuracy, area under the curve (AUC) were used as primary evaluation metrics.

Results

A total of 13,684 images of 3447 female patients are finally included. In external test the 224 and 320 REZ achieve the best performance in MobileNet and EfficientNetB0 respectively (AUC: 0.893 and 0.907). Meanwhile, 448 REZ achieve the best performance in Xception, DenseNet121 and ResNet50 (AUC: 0.900, 0.883 and 0.871 respectively). In physician-AI test set, the 320 REZ for EfficientNetB0 (AUC: 0.896, P < 0.1) is better than senior physicians. Besides, the 224 REZ for MobileNet (AUC: 0.878, P < 0.1), 448 REZ for Xception (AUC: 0.895, P < 0.1) are better than junior physicians. While the 448 REZ for DenseNet121 (AUC: 0.880, P < 0.05) and ResNet50 (AUC: 0.838, P < 0.05) are only better than entry physicians.

Conclusion

Based on the gray-scale ultrasound breast images, we obtained the best DL combination which was better than the physicians.

Synergistic Manipulation of Interdependent Thermoelectric Parameters in SnTe-AgBiTe2 Alloys by Mn Doping

In the mid-temperature region, SnTe is a promising substitute for PbTe, whereas the thermoelectric (TE) property of pristine SnTe is severely limited by the good thermal conductivity and inferior Seebeck coefficient. In this research, we synergistically manipulate the interdependent TE parameters of SnTe-AgBiTe₂ alloys by Mn doping to increase the ZT value. The AgBiTe₂ alloying is found to greatly reduce the electrical conductivity and electronic contribution for thermal transport by reducing the carrier mobility, while Mn doping obviously improves the Seebeck coefficient by effectively decreasing the valence band offset. The lowest κ_l of Mn-doped SnTe-AgBiTe₂ alloys is 0.49 W m^-1 K^-1 at 823 K since the various defects strengthen the phonon scattering. Collectively, these manipulations yield a peak ZT value of 1.40 at 823 K and an average ZT value of 0.73 (300-823 K) in the Mn-doped SnTe-AgBiTe₂ alloys. This research suggests that Mn doping is a valid scheme to constantly improve the thermoelectric property of SnTe-AgBiTe₂ alloys in a wide temperature range.