REV-ERB in GABAergic neurons controls diurnal hepatic insulin sensitivity.

Systemic insulin sensitivity shows a diurnal rhythm with a peak upon waking1,2. The molecular mechanism that underlies this temporal pattern is unclear. Here we show that the nuclear receptors REV-ERB-α and REV-ERB-ß (referred to here as 'REV-ERB') in the GABAergic (γ-aminobutyric acid-producing) neurons in the suprachiasmatic nucleus (SCN) (SCNGABA neurons) control the diurnal rhythm of insulin-mediated suppression of hepatic glucose production in mice, without affecting diurnal eating or locomotor behaviours during regular light-dark cycles. REV-ERB regulates the rhythmic expression of genes that are involved in neurotransmission in the SCN, and modulates the oscillatory firing activity of SCNGABA neurons. Chemogenetic stimulation of SCNGABA neurons at waking leads to glucose intolerance, whereas restoration of the temporal pattern of either SCNGABA neuron firing or REV-ERB expression rescues the time-dependent glucose metabolic phenotype caused by REV-ERB depletion. In individuals with diabetes, an increased level of blood glucose after waking is a defining feature of the 'extended dawn phenomenon'3,4. Patients with type 2 diabetes with the extended dawn phenomenon exhibit a differential temporal pattern of expression of REV-ERB genes compared to patients with type 2 diabetes who do not have the extended dawn phenomenon. These findings provide mechanistic insights into how the central circadian clock regulates the diurnal rhythm of hepatic insulin sensitivity, with implications for our understanding of the extended dawn phenomenon in type 2 diabetes.

CIB2 Is a Novel Endogenous Repressor of Atrial Remodeling.

BACKGROUND: Atrial fibrillation (AF) is a highly prevalent condition that can cause or exacerbate heart failure, is an important risk factor for stroke, and is associated with pronounced morbidity and death. Genes uniquely expressed in the atria are known to be essential for maintaining atrial structure and function. Atrial tissue remodeling contributes to arrhythmia recurrence and maintenance. However, the mechanism underlying atrial remodeling remains poorly understood. This study was designed to investigate whether other uncharacterized atrial specific genes play important roles in atrial physiology and arrhythmogenesis. METHODS: RNA-sequencing analysis was used to identify atrial myocyte specific and angiotensin II-responsive genes. Genetically modified, cardiomyocyte-specific mouse models (knockout and overexpression) were generated. In vivo and in vitro electrophysiological, histology, and biochemical analyses were performed to determine the consequences of CIB2 (calcium and integrin binding family member 2 protein) gain and loss of function in the atrium. RESULTS: Using RNA-sequencing analysis, we identified CIB2 as an atrial-enriched protein that is significantly downregulated in the left atria of patients with AF and mouse models of AF from angiotensin II infusion or pressure overload. Using cardiomyocyte-specific Cib2 knockout (Cib2-/-) and atrial myocyte-specific Cib2-overexpressing mouse models, we found that loss of Cib2 enhances AF occurrence, prolongs AF duration, and correlates with a significant increase in atrial fibrosis under stress. Conversely, Cib2 overexpression mitigates AF occurrence and atrial fibrosis triggered by angiotensin II stress. Mechanistically, we revealed that CIB2 competes with and inhibits CIB1-mediated calcineurin activation, thereby negating stress-induced structural remodeling and AF. CONCLUSIONS: Our data suggest that CIB2 represents a novel endogenous and atrial-enriched regulator that protects against atrial remodeling and AF under stress conditions. Therefore, CIB2 may represent a new potential target for treating AF.

High Performance Thermoelectric Power of Bi0.5Sb1.5Te3 Through Synergistic Cu2GeSe3 and Se Incorporations.

Bi2Te3-based alloys are the benchmark for commercial thermoelectric (TE) materials, the widespread demand for low-grade waste heat recovery and solid-state refrigeration makes it imperative to enhance the figure-of-merits. In this study, high-performance Bi0.5Sb1.5Te3 (BST) is realized by incorporating Cu2GeSe3 and Se. Concretely, the diffusion of Cu and Ge atoms optimizes the hole concentration and raises the density-of-states effective mass (md *), compensating for the loss of "donor-like effect" exacerbated by ball milling. The subsequent Se addition further increases md *, enabling a total 28% improvement of room-temperature power factor (S2σ), reaching 43.6 µW cm-1 K-2 compared to the matrix. Simultaneously, the lattice thermal conductivity is also significantly suppressed by multiscale scattering sources represented by Cu-rich nanoparticles and dislocation arrays. The synergistic effects yield a peak ZT of 1.41 at 350 K and an average ZT of 1.23 (300-500 K) in the Bi0.5Sb1.5Te2.94Se0.06 + 0.11 wt.% Cu2GeSe3 sample. More importantly, the integrated 17-pair TE module achieves a conversion efficiency of 6.4%, 80% higher than the commercial one at ΔT = 200 K. These results validate that the facile composition optimization of the BST/Cu2GeSe3/Se is a promising strategy to improve the application of BST-based TE modules.

In-band pumped Kerr-lens mode-locked Tm,Ho-codoped calcium aluminate laser.

We report on a Kerr-lens mode-locked Tm,Ho-codoped calcium aluminate laser with in-band pumping of the Tm ions by a spatially single-mode 1678 nm Raman fiber laser. The structurally disordered CaGdAlO4 host crystal is also codoped also with the passive Lu ion for additional inhomogeneous line broadening. The Tm,Ho,Lu:CaGdAlO4 laser generates soliton pulses as short as 79 fs at a central wavelength of 2073.6 nm via soft-aperture Kerr-lens mode-locking. The corresponding average output power amounts to 91 mW at a pulse repetition rate of ∼86 MHz. The average output power can be scaled to 842 mW at the expense of slightly longer pulses of 155 fs at 2045.9 nm, which corresponds to a peak power of ∼58 kW. To the best of our knowledge, this represents the first demonstration of an in-band pumped Kerr-lens mode-locked Tm,Ho solid-state laser at ∼2 µm.

Association between left atrial myopathy and sarcomere mutation in patients with hypertrophic cardiomyopathy: insights into left atrial strain by MRI feature tracking.

OBJECTIVES: Left atrial (LA) myopathy, characterized by LA enlargement and mechanical dysfunction, is associated with worse prognosis in hypertrophic cardiomyopathy (HCM) while the impact of sarcomere mutation on LA myopathy remains unclear. We aimed to assess the association between LA myopathy and sarcomere mutation and to explore the incremental utility of LA strain in mutation prediction. METHODS: A total of 105 consecutive HCM patients (mean age 47.8 ± 11.9 years, 71% male) who underwent HCM-related gene screening and cardiac MRI were retrospectively enrolled. LA volume, ejection fraction and strain indices in reservoir, conduit, and booster-pump phases were investigated respectively. RESULTS: Fifty mutation-positive patients showed higher LA maximal volume index (59.4 ± 28.2 vs 43.8 ± 18.1 mL/m2, p = 0.001), lower reservoir (21.3 ± 7.9 vs 26.2 ± 6.6%, p < 0.001), and booster-pump strain (12.1 ± 5.4 vs 17.1 ± 5.0%, p < 0.001) but similar conduit strain (9.2 ± 4.5 vs 9.1 ± 4.5%, p = 0.909) compared with mutation-negative patients. In multivariate logistic regression, LA booster-pump strain was associated with sarcomere mutation (odds ratio = 0.86, 95% confidence interval: 0.77-0.96, p = 0.010) independent of maximal wall thickness, late gadolinium enhancement, and LA volume. Furthermore, LA booster-pump strain showed incremental value for mutation prediction added to Mayo II score (AUC 0.798 vs 0.709, p = 0.024). CONCLUSIONS: In HCM, mutation-positive patients suffered worse LA enlargement and worse reservoir and booster-pump functions. LA booster-pump strain was a strong factor for sarcomere mutation prediction added to Mayo II score. CLINICAL RELEVANCE STATEMENT: The independent association between sarcomere mutation and left atrial mechanical dysfunction provide new insights into the pathogenesis of atrial myopathy and is helpful to understand the adverse prognosis regarding atrial fibrillation and stroke in mutation-positive patients. KEY POINTS: • In patients with hypertrophic cardiomyopathy, left atrial (LA) reservoir and booster-pump function, but not conduit function, were significantly impaired in mutation-positive patients compared with mutation-negative patients. • LA booster-pump strain measured by MRI-derived feature tracking is feasible to predict sarcomere mutation with high incremental value added to Mayo II score.

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.

Assessment of late gadolinium enhancement in hypertrophic cardiomyopathy improves risk stratification based on current guidelines.

BACKGROUND AND AIMS: Identifying patients with hypertrophic cardiomyopathy (HCM) who are candidates for implantable cardioverter defibrillator (ICD) implantation in primary prevention for sudden cardiac death (SCD) is crucial. The aim of this study was to externally validate the 2022 European Society of Cardiology (ESC) model and other guideline-based ICD class of recommendation (ICD-COR) models and explore the utility of late gadolinium enhancement (LGE) in further risk stratification. METHODS: Seven hundred and seventy-four consecutive patients who underwent cardiac magnetic resonance imaging were retrospectively enrolled. RESULTS: Forty-six (5.9%) patients reached the SCD-related endpoint during 7.4 ± 2.5 years of follow-up. Patients suffering from SCD had higher ESC Risk-SCD score (4.3 ± 2.4% vs. 2.8 ± 2.1%, P < .001) and LGE extent (13.7 ± 9.4% vs. 4.9 ± 6.6%, P < .001). Compared with the 2014 ESC model, the 2022 ESC model showed increased area under the curve (.76 vs. .63), sensitivity (76.1% vs. 43.5%), positive predictive value (16.8% vs. 13.6%), and negative predictive value (98.1% vs. 95.9%). The C-statistics for SCD prediction of 2011 American College of Cardiology (ACC)/American Heart Association (AHA), 2014 ESC, 2020 AHA/ACC, and 2022 ESC models were .68, .64, .76 and .78, respectively. Furthermore, in patients without extensive LGE, LGE ≥5% was responsible for seven-fold SCD risk after multivariable adjustment. Whether in ICD-COR II or ICD-COR III, patients with LGE ≥5% and <15% showed significantly worse prognosis than those with LGE <5% (all P < .001). CONCLUSIONS: The 2022 ESC model performed better than the 2014 ESC model with especially improved sensitivity. LGE enabled further risk stratification based on current guidelines.

Biodegradation of various grades of polyethylene microplastics by Tenebrio molitor and Tenebrio obscurus larvae: Effects on their physiology.

Polyethylene (PE) is the most productive plastic product and includes three major polymers including high-density polyethylene (HDPE), linear low-density polyethylene (LLDPE) and low-density polyethylene (LDPE) variation in the PE depends on the branching of the polymer chain and its crystallinity. Tenebrio obscurus and Tenebrio molitor larvae biodegrade PE. We subsequently tested larval physiology, gut microbiome, oxidative stress, and PE degradation capability and degradation products under high-purity HDPE, LLDPE, and LDPE powders (<300 µm) diets for 21 days at 65 ± 5% humidity and 25 ± 0.5 °C. Our results demonstrated the specific PE consumption rates by T. molitor was 8.04-8.73 mg PE ∙ 100 larvae-1⋅day-1 and by T. obscurus was 7.68-9.31 for LDPE, LLDPE and HDPE, respectively. The larvae digested nearly 40% of the ingested three PE and showed similar survival rates and weight changes but their fat content decreased by 30-50% over 21-day period. All the PE-fed groups exhibited adverse effects, such as increased benzoquinone concentrations, intestinal tissue damage and elevated oxidative stress indicators, compared with bran-fed control. In the current study, the digestive tract or gut microbiome exhibited a high level of adaptability to PE exposure, altering the width of the gut microbial ecological niche and community diversity, revealing notable correlations between Tenebrio species and the physical and chemical properties (PCPs) of PE-MPs, with the gut microbiome and molecular weight change due to biodegradation. An ecotoxicological simulation by T.E.S.T. confirmed that PE degradation products were little ecotoxic to Daphnia magna and Rattus norvegicus providing important novel insights for future investigations into the environmentally-friendly approach of insect-mediated biodegradation of persistent plastics.

Didymin alleviates metabolic dysfunction-associated fatty liver disease (MAFLD) via the stimulation of Sirt1-mediated lipophagy and mitochondrial biogenesis.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is one of the most prevalent metabolic syndromes worldwide. However, no approved pharmacological treatments are available for MAFLD. Chenpi, one kind of dried peel of citrus fruits, has traditionally been utilized as a medicinal herb for liver diseases. Didymin is a newly identified oral bioactive dietary flavonoid glycoside derived from Chenpi. In this study, we investigated the therapeutic potential of Didymin as an anti-MAFLD drug and elucidated its underlying mechanisms. METHODS: High-fat diet (HFD)-induced MAFLD mice and alpha mouse liver 12 (AML12) cells were utilized to evaluate the effects and mechanisms of Didymin in the treatment of MAFLD. Liver weight, serum biochemical parameters, and liver morphology were examined to demonstrate the therapeutic efficacy of Didymin in MAFLD treatment. RNA-seq analysis was performed to identify potential pathways that could be affected by Didymin. The impact of Didymin on Sirt1 was corroborated through western blot, molecular docking analysis, microscale thermophoresis (MST), and deacetylase activity assay. Then, a Sirt1 inhibitor (EX-527) was utilized to confirm that Didymin alleviates MAFLD via Sirt1. Western blot and additional assays were used to investigate the underlying mechanisms. RESULTS: Our results suggested that Didymin may possess therapeutic potential against MAFLD in vitro and in vivo. By promoting Sirt1 expression as well as directly binding to and activating Sirt1, Didymin triggers downstream pathways that enhance mitochondrial biogenesis and function while reducing apoptosis and enhancing lipophagy. CONCLUSIONS: These suggest that Didymin could be a promising medication for MAFLD treatment. Furthermore, its therapeutic effects are mediated by Sirt1.

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.

cGAS-STING mediates cytoplasmic mitochondrial-DNA-induced inflammatory signal transduction during accelerated senescence of pancreatic ß-cells induced by metabolic stress.

Type 2 diabetes mellitus (T2DM) is an age-related disease characterized by impaired pancreatic ß cell function and insulin resistance. Recent studies have shown that the accumulation of senescent ß cells under metabolic stress conditions leads to the progression of T2DM, while senolysis can improve the prognosis. However, the specific mechanism of ß cell senescence is still unclear. In this study, we found that the increased load of senescence pancreatic ß cells in both older mice and obese mice induced by high-fat diet (HFD) (DIO mice) was accompanied by activation of the Cyclic GMP-AMP synthase (cGAS) - stimulator of interferon genes (STING) pathway and using cGAS or STING small interfering RNA or STING inhibitor C176 to downregulate this pathway reduced the senescence-associated secretion profile (SASP) and senescence of Min6 cells treated with palmitic acid or hydrogen peroxide. C176 intervention in DIO mice also significantly reduced the inflammation and senescence of the islets, thereby protecting the function of pancreatic ß cell and glucose metabolism. Our study further revealed that mitochondrial DNA (mtDNA) leakage under metabolic stress conditions was critical for the activation of the cGAS-STING pathway, which can be reversed by the mtDNA depleting agent ethidium bromide. Consistently, mtDNA leakage was more severe in older mice and was accelerated by a chronic HFD. In conclusion, we demonstrate that cytoplasmic mtDNA activates the cGAS-STING pathway to mediate SASP during the accelerated senescence of pancreatic ß-cells induced by metabolic stress, and this process can be downregulated by the STING inhibitor C176.

Heterologous Isomorphic Substitution Induces Optical Property Enhancement for Deep-UV Crystals: a Case in Rb[B3O3F2(OH)2].

Optical anisotropy is pivotal for optical crystals, and it can be characterized by the maximum algebraic difference in refractive indices. Improving the optical anisotropy, especially for deep-ultraviolet (UV) crystals, is still a challenge and of interest. Herein, a new hydroxyfluorooxoborate, Rb[B3O3F2(OH)2], was obtained by the heterologous isomorphic substitution strategy. Dual enhancement for the band gap and birefringence compared with the parent A[B3O3F2(OH)2] (A = [Ph4P]/[Ph3MeP]) compounds was achieved in Rb[B3O3F2(OH)2]. This considerable enhancement originates from the removal of organic components and the retention of a birefringence-active anionic framework. This enhancement pushes the application region from UV to deep-UV. This discovery not only expands the structural chemistry of borates but also demonstrates the viability of heterologous isomorphic substitution to design deep-UV crystals with enhanced optical property.

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.

Signal-amplified electrochemiluminescence aptasensor for mucin 1 determination using CdS QDs/g-C3N4 and Au NPs@TEOA.

A novel electrochemiluminescence (ECL) aptasensor, using graphite carbonitride (g-C3N4) capped CdS quantum dots (CdS QDs@g-C3N4) and Au nanoparticles decorated triethanolamine (AuNPs@TEOA) as dual coreactants, was proposed for the determination of mucin 1 (MUC1). Higher ECL efficiency was acquired due to the double enhancement contribution of CdS QDs and TEOA to Ru (bpy)32+ ECL. Additionally, AuNPs@TEOA also acted as nanocarrier for MUC1 aptamer immobilization. After the aptasensor was incubated in target MUC1, the decreased ECL emission was obtained because of the poor conductivity of MUC1. The ECL aptasensor displayed a good linear correlation for MUC1 in the range 0.1 pg mL-1 -1000 ng mL-1, and the detection limit was 33 fg mL-1. MUC1 spiked into human serum samples was quantified to assess the practicability of the ECL aptasensor.

Carbon Dots-Based Fluorescence Assay for the Facile and Reliable Detection of Ag+ in Natural Water and Serum Samples.

In this report, red-emissive carbon dots (C-dots) were facilely prepared from o-phenylenediamine via microwave-assisted hydrothermal treatment. The C-dots demonstrated excitation wavelength-independent emission with maximums at 621 nm that could be effectively quenched by Ag+ via static quenching. This phenomenon was exploited to establish a sensitive fluorescence assay with a low detection limit (0.37 µM) and wide linear range (0-50 µM). In addition, this assay demonstrated excellent selectivity toward Ag+, free from the interference of 16 commonly seen metal ions. Most importantly, the assay demonstrated high reliability toward samples in deionized water, mineral water, lake water, and serum, which could indicate potential applications for Ag+ monitoring in complicated natural and biological environments.

Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug-drug links based on graph neural network.

MOTIVATION: Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. RESULTS: In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. AVAILABILITYAND IMPLEMENTATION: The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Melatonin prevents diabetes-associated cognitive dysfunction from microglia-mediated neuroinflammation by activating autophagy via TLR4/Akt/mTOR pathway.

Cognitive dysfunction often occurs in diabetes mellitus patients. This study aimed to investigate the efficacy of melatonin (MLT) in improving diabetes-associated cognitive decline and the underlying mechanism involved. Type 2 diabetic mice and palmitic acid (PA)-stimulated BV-2 cells were treated by MLT, and the potential mechanisms among MLT, cognition, and autophagy were explored. The results showed that type 2 diabetic mice showed obvious learning and memory impairments in the Morris water maze test compared with normal controls, which could be ameliorated by MLT treatment. Meanwhile, MLT administration significantly improved neuroinflammation and regulated microglial apoptosis. Furthermore, autophagy inhibitor 3-methyladenine (3-MA) increased the microglial inflammation and apoptosis, indicating that the treatment effect of MLT was mediated by autophagy. Lastly, MLT treatment significantly decreased the levels of toll-like receptors 4 (TLR4), phosphorylated-protein kinase B (Akt), and phosphorylated-mechanistic target of rapamycin (mTOR), indicating that blocking TLR4/Akt/mTOR pathway might be an underlying basis for the anti-inflammatory and anti-apoptosis effects of MLT. Collectively, our study suggested that MLT could improve learning and memory in type 2 diabetic mice by activating autophagy via the TLR4/Akt/mTOR pathway, thereby inhibiting neuroinflammation and microglial apoptosis.