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"Jiang, Jue"
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Effect of the inhaled anesthetics isoflurane, sevoflurane and desflurane on the neuropathogenesis of Alzheimer's disease (Review)
The incidence of Alzheimer's disease (AD) in individuals >65 years of age is 13% and ~66 million individuals in this age group undergo surgery annually under anesthesia. It is therefore important to determine whether commonly used inhaled anesthetics induce cytotoxicity, which may lead to neurodegeneration. Findings from several studies suggest that the anesthetics, isoflurane, sevoflurane and desflurane, may activate caspases, increase the synthesis and accumulation of β-amyloid (Aβ) protein, and induce hyperphosphorylation of tau proteins, all of which are cellular responses consistent with the neuropathogenesis of AD. Other studies have arrived at different and occasionally contradictory conclusions. The present review attempts to resolve this discrepancy by reviewing previous studies, which have investigated the effects of commonly used inhaled anesthetics on the synthesis and accumulation of Aβ, tau pathology and cognitive function. The possible underlying mechanism was also reviewed. However, several aspects of this phenomenon remain to be elucidated. Further studies are required to fully examine anesthesia-induced neurotoxicity and elucidate the effect of inhaled anesthetics on the onset and progression of AD.
Journal Article
Absence of evidence for chiral Majorana modes in quantum anomalous Hall-superconductor devices
A quantum anomalous Hall (QAH) insulator coupled to an s-wave superconductor is predicted to harbor chiral Majorana modes. A recent experiment interprets the half-quantized two-terminal conductance plateau as evidence for these modes in a millimeter-size QAH-niobium hybrid device. However, non-Majorana mechanisms can also generate similar signatures, especially in disordered samples. Here, we studied similar hybrid devices with a well-controlled and transparent interface between the superconductor and the QAH insulator. When the devices are in the QAH state with well-aligned magnetization, the two-terminal conductance is always half-quantized. Our experiment provides a comprehensive understanding of the superconducting proximity effect observed in QAH-superconductor hybrid devices and shows that the half-quantized conductance plateau is unlikely to be induced by chiral Majorana fermions in samples with a highly transparent interface.
Journal Article
An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules
Objectives
From the viewpoint of ultrasound (US) physicians, an ideal thyroid US computer-assisted diagnostic (CAD) system for thyroid cancer should perform well in suspicious thyroid nodules with atypical risk features and be able to output explainable results. This study aims to develop an explainable US CAD model for suspicious thyroid nodules.
Methods
A total of 2992 solid or almost-solid thyroid nodules were analyzed retrospectively. All nodules had pathological results (1070 malignancies and 1992 benignities) confirmed by ultrasound-guided fine-needle aspiration cytology and histopathology after thyroidectomy. A deep learning model (ResNet50) and a multiple risk features learning ensemble model (XGBoost) were used to train the US images of 2794 thyroid nodules. Then, an integrated AI model was generated by combining both models. The diagnostic accuracies of the three AI models (ResNet50, XGBoost, and the integrated model) were predicted in a testing set including 198 thyroid nodules and compared to the diagnostic efficacy of five ultrasonographers.
Results
The accuracy of the integrated model was 76.77%, while the mean accuracy of the ultrasonographers was 68.38%. Of the risk features, microcalcifications showed the highest contribution to the diagnosis of malignant nodules.
Conclusions
The integrated AI model in our study can improve the diagnostic accuracy of suspicious thyroid nodules and output the known risk features simultaneously, thus aiding in training young ultrasonographers by linking the explainable results to their clinical experience and advancing the acceptance of AI diagnosis for thyroid cancer in clinical practice.
Key Points
• We developed an artificial intelligence (AI) diagnosis model based on both deep learning and multiple risk feature ensemble learning methods.
• The AI diagnosis model showed higher diagnostic accuracy for suspicious thyroid nodules than ultrasonographers.
• The AI diagnosis model showed partial explainability by outputting the known risk features, thus aiding young ultrasonic doctors in increasing the diagnostic level for thyroid cancer.
Journal Article
Atomically thin half-van der Waals metals enabled by confinement heteroepitaxy
Atomically thin two-dimensional (2D) metals may be key ingredients in next-generation quantum and optoelectronic devices. However, 2D metals must be stabilized against environmental degradation and integrated into heterostructure devices at the wafer scale. The high-energy interface between silicon carbide and epitaxial graphene provides an intriguing framework for stabilizing a diverse range of 2D metals. Here we demonstrate large-area, environmentally stable, single-crystal 2D gallium, indium and tin that are stabilized at the interface of epitaxial graphene and silicon carbide. The 2D metals are covalently bonded to SiC below but present a non-bonded interface to the graphene overlayer; that is, they are ‘half van der Waals’ metals with strong internal gradients in bonding character. These non-centrosymmetric 2D metals offer compelling opportunities for superconducting devices, topological phenomena and advanced optoelectronic properties. For example, the reported 2D Ga is a superconductor that combines six strongly coupled Ga-derived electron pockets with a large nearly free-electron Fermi surface that closely approaches the Dirac points of the graphene overlayer.
Single-crystal 2D metals are stabilized at the interface between epitaxial graphene and silicon carbide, with strong internal gradients in bonding character. The confined 2D metals demonstrate compelling superconducting properties.
Journal Article
The Relationship between Ovarian Ultrasound Parameters and Endocrine and Metabolic Indicators in Patients with Ovarian Syndrome
Objective. The aim of this study is to investigate the relationship between the levels of endocrine and metabolic indicators and ovarian ultrasonography indicators in patients with ovarian syndrome (PCOS). Methods. Three hundred and forty patients with PCOS from January 2017 to February 2022 were selected as the observation group, and 340 healthy women of the same age were selected as the control group. A retrospective analysis was performed to observe the levels of endocrine and metabolic indicators and ovarian ultrasound examination indicators in the two groups. Results. The levels of testosterone, dehydroepiandrosterone (DHEA-S), luteinizing hormone (LH), LH/FSH, blood glucose, and insulin were higher in the observation group than those in the control group. The levels of low-density lipoprotein (LDL) and free insulin-like growth factor (IGF-I) were higher in the observation group than those in the control group. However, the level of high-density lipoprotein (HDL) was lower in the observation group than that in the control group. The ovarian interstitial area, total ovarian area, ovarian volume, number of follicles, uterine artery pulsatility index (PI), and resistance index (RI) were higher in the observation group than those in the control group. Pearson correlation analysis concluded that estrone (E1) levels in PCOS patients were correlated with ovarian interstitial area, total ovarian area, and ovarian volume. In addition, E1 levels correlated with LH levels, LH/FSH, testosterone, DHEA-S, and progesterone at P<0.05. Compared with different treatment methods, the total testosterone, LH, and LH/FSH levels in the two groups were decreased compared with those before treatment, and the degree of decrease in the combined treatment group was more significant than that in the treatment alone group. Conclusion. The levels of endocrine metabolism and ovarian ultrasound in PCOS patients are abnormal and there is a close relationship between the levels of endocrine metabolism and ovarian ultrasound. Attention should be paid to the monitoring and regulation of endocrine metabolism and ovarian ultrasound. Integrated traditional Chinese and western medicine can greatly improve the hormone levels in PCOS patients.
Journal Article
Concurrence of quantum anomalous Hall and topological Hall effects in magnetic topological insulator sandwich heterostructures
The quantum anomalous Hall (QAH) effect is a consequence of non-zero Berry curvature in momentum space. The QAH insulator harbours dissipation-free chiral edge states in the absence of an external magnetic field. However, the topological Hall (TH) effect, a hallmark of chiral spin textures, is a consequence of real-space Berry curvature. Here, by inserting a topological insulator (TI) layer between two magnetic TI layers, we realized the concurrence of the TH effect and the QAH effect through electric-field gating. The TH effect is probed by bulk carriers, whereas the QAH effect is characterized by chiral edge states. The appearance of the TH effect in the QAH insulating regime is a consequence of chiral magnetic domain walls that result from the gate-induced Dzyaloshinskii–Moriya interaction and occurs during the magnetization reversal process in the magnetic TI sandwich samples. The coexistence of chiral edge states and chiral spin textures provides a platform for proof-of-concept dissipationless spin-textured spintronic applications.
The coexistence of chiral edge states and chiral spin textures in magnetic topological insulator sandwiches provides a platform for proof-of-concept dissipationless spin-textured spintronic applications.
Journal Article
A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients
Objective
Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC).
Methods
Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs.
Results
Calcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (
p
< 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone.
Conclusions
Our models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail.
Clinical relevance statement
Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice.
Key Points
• We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images.
• Three novel parameters for quantifying US calcifications were defined and verified.
• These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients.
Journal Article
DCATNet: polyp segmentation with deformable convolution and contextual-aware attention network
Polyp segmentation is crucial in computer-aided diagnosis but remains challenging due to the complexity of medical images and anatomical variations. Current state-of-the-art methods struggle with accurate polyp segmentation due to the variability in size, shape, and texture. These factors make boundary detection challenging, often resulting in incomplete or inaccurate segmentation. To address these challenges, we propose DCATNet, a novel deep learning architecture specifically designed for polyp segmentation. DCATNet is a U-shaped network that combines ResNetV2-50 as an encoder for capturing local features and a Transformer for modeling long-range dependencies. It integrates three key components: the Geometry Attention Module (GAM), the Contextual Attention Gate (CAG), and the Multi-scale Feature Extraction (MSFE) block. We evaluated DCATNet on five public datasets. On Kvasir-SEG and CVC-ClinicDB, the model achieved mean dice scores of 0.9351 and 0.9444, respectively, outperforming previous state-of-the-art (SOTA) methods. Cross-validation further demonstrated its superior generalization capability. Ablation studies confirmed the effectiveness of each component in DCATNet. Integrating GAM, CAG, and MSFE effectively improves feature representation and fusion, leading to precise and reliable segmentation results. These findings underscore DCATNet’s potential for clinical application and can be used for a wide range of medical image segmentation tasks.
Journal Article
Transcriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto’s thyroiditis
While ultrasonography effectively diagnoses Hashimoto’s thyroiditis (HT), exploring its transcriptomic landscape could reveal valuable insights into disease mechanisms. This study aimed to identify HT-associated RNA signatures and investigate their potential for enhanced molecular characterization. Samples comprising 31 HT patients and 30 healthy controls underwent RNA sequencing of peripheral blood. Differential expression analysis identified transcriptomic features, which were integrated using multi-omics factor analysis. Pathway enrichment, co-expression, and regulatory network analyses were performed. A novel machine-learning model was developed for HT molecular characterization using stacking techniques. HT patients exhibited increased thyroid volume, elevated tissue hardness, and higher antibody levels despite being in the early subclinical stage. Analysis identified 79 HT-associated transcriptomic features (3 mRNA, 6 miRNA, 64 lncRNA, 6 circRNA). Co-expression (77 nodes, 266 edges) and regulatory (18 nodes, 45 edges) networks revealed significant hub genes and modules associated with HT. Enrichment analysis highlighted dysregulation in immune system, cell adhesion and migration, and RNA/protein regulation pathways. The novel stacking-model achieved 95% accuracy and 97% AUC for HT molecular characterization. This study demonstrates the value of transcriptome analysis in uncovering HT-associated signatures, providing insights into molecular changes and potentially guiding future research on disease mechanisms and therapeutic strategies.
Journal Article