MiR-495-3p promotes cardiac hypertrophy by targeting Pum2.

Pathological cardiac hypertrophy (CH) may lead to heart failure and sudden death. MicroRNAs (miRNAs) have been documented to play crucial parts in CH. The objective of this research was to discuss the potential along with molecule mechanism of miR-495-3p in CH. In vivo CH model was induced by aortic banding (AB) in rats. Cellular hypertrophy in H9c2 rat cardiomyocytes was stimulated by angiotensin II (Ang II) treatment. Haematoxylin and eosin (HE), echocardiography and immunofluorescence staining were used to examine the alterations in cardiac function. The outcomes showed that miR-495-3p expression was high in rat model as well as in Ang II-stimulated cardiomyocytes. Besides, silenced miR-495-3p attenuated CH both in vitro and in vivo. Mechanically, miR-495-3p bound to pumilio RNA binding family member 2 (Pum2) 3'UTR and silenced its expression. Rescue assays further notarized that Pum2 silence abrogated the inhibitory impacts of miR-495-3p inhibitor on CH. In a word, the present research uncovered that miR-495-3p promoted CH by targeting Pum2. Therefore, miR-495-3p may be a novel therapeutic molecule for this disease.

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.

OBJECTIVES: Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers. METHODS: This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps. RESULTS: The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively. CONCLUSIONS: By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.

SHP2 ablation mitigates osteoarthritic cartilage degeneration by promoting chondrocyte anabolism through SOX9.

Articular cartilage phenotypic homeostasis is crucial for life-long joint function, but the underlying cellular and molecular mechanisms governing chondrocyte stability remain poorly understood. Here, we show that the protein tyrosine phosphatase SHP2 is differentially expressed in articular cartilage (AC) and growth plate cartilage (GPC) and that it negatively regulates cell proliferation and cartilage phenotypic program. Postnatal SHP2 deletion in Prg4+ AC chondrocytes increased articular cellularity and thickness, whereas SHP2 deletion in Acan+ pan-chondrocytes caused excessive GPC chondrocyte proliferation and led to joint malformation post-puberty. These observations were verified in mice and in cultured chondrocytes following treatment with the SHP2 PROTAC inhibitor SHP2D26. Further mechanistic studies indicated that SHP2 negatively regulates SOX9 stability and transcriptional activity by influencing SOX9 phosphorylation and promoting its proteasome degradation. In contrast to published work, SHP2 ablation in chondrocytes did not impact IL-1-evoked inflammation responses, and SHP2's negative regulation of SOX9 could be curtailed by genetic or chemical SHP2 inhibition, suggesting that manipulating SHP2 signaling has translational potential for diseases of cartilage dyshomeostasis.

Can similarity of autistic traits promote neural synchronization?

People with similar levels of autistic traits are reported to exhibit better interactions than those with larger differences in autistic traits. However, whether this "similarity effect" exists at the neural level remains unclear. To address this gap, the present study employed functional near-infrared spectroscopy (fNIRS) hyperscanning technology to assess inter-brain synchronization (IBS) during naturalistic conversations among dyads with three types of autistic trait combinations (20 high-high, 22 high-low, and 18 low-low dyads). The results revealed that the high-high dyads exhibited significantly lower IBS in the right temporoparietal junction (rTPJ) region compared to the low-low dyads, with no significant differences observed between the high-low group and the other two groups. Moreover, though dyadic differences in conversation satisfaction were positively correlated with dyadic autistic trait differences, IBS only showed a significant negative correlation with the dyadic average autistic trait scores and no significant correlation with the dyadic difference scores of autistic traits. These findings suggest that dyads with high autistic traits may have shared feelings about conversations, but cannot produce IBS through successful mutual prediction and understanding.

Development and validation of a CT based radiomics nomogram for preoperative prediction of ISUP/WHO grading in renal clear cell carcinoma.

BACKGROUND: Nuclear grading of clear cell renal cell carcinoma (ccRCC) is crucial for its diagnosis and treatment. OBJECTIVE: To develop and validate a machine learning model for preoperative assessment of ccRCC nuclear grading using CT radiomics. MATERIALS AND METHODS: This retrospective study analyzed 146 ccRCC patients who underwent surgery between June 2016 and January 2022 at two hospitals (the Quzhou Affiliated Hospital of Wenzhou Medical University with 117 cases and the Affiliated Cancer Hospital of University of Chinese Academy of Sciences with 29 cases). Radiomic features were extracted from preoperative abdominal CT images. Features reduction and selection were carried out using intraclass correlation efficient (ICCs), Spearman rank correlation coefficientsand and the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Radiomics and clinical models were developed utilizing Support Vector Machine (SVM), Extremely Randomized Trees (Extra Trees), Light Gradient Boosting Machine (LightGBM), Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. Subsequently, the radiomics nomogramwas developed incorporating independent clinical predictors and Rad_signature. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity, with decision curve analysis (DCA) assessing its clinical utility. RESULTS: We extracted 1834 radiomic features from each CT sequence, with 1320 features passing through the ICCs screening process. 480 radiomics features were screened by Spearson correlation coefficient. Then, 15 radiomic features with non-zero coefficient values were determined by Lasso dimensionality reduction technique. The five machine learning methods effectively distinguished nuclear grades. The radiomics nomogram outperformed clinical radiological models and radiomics feature models in predictive performance, with an AUC of 0.936 (95% CI 0.885-0.986) for the training set and 0.896 (95% CI 0.716-1.000) for the external verification set. DCA indicated potential clinical applicability of the nomogram. CONCLUSION: The radiomics nomogram, developed by integrating clinically independent risk factors and and Rad_signature, demonstrated robust performance in preoperative ccRCC grading. It offers a non-invasive tool that aids in ccRCC grading and clinical decision-making, with potential to enhance treatment strategies.

Discovery of KW0113 as a First and Effective PROTAC Degrader of DNMT1 Protein.

DNA methyltransferase 1 (DNMT1) is an attractive therapeutic target for acute myelocytic leukemia (AML) and other malignancies. It has been reported that the genetic depletion of DNMT1 inhibited AML cell proliferation through reversing DNA methylation abnormalities. However, no DNMT1-targeted PROTAC degraders have been reported yet. Herein, a series of proteolysis-targeting chimera (PROTAC) degrader of DNMT1 based on dicyanopyridine scaffold and VHL E3 ubiquitin ligase ligand was developed. Among them, KW0113 (DC50 = 643/899 nM in MV4-11/MOLM-13 cells) exhibited optimal DNMT1 degradation. KW0113 induced DNMT1-selective degradation in a dose- and time-dependent manner through VHL engagement. Moreover, KW0113 inhibited AML cell growth by reversing promoter DNA hypermethylation and tumor-suppressor genes silencing. In conclusion, these findings proved the capability of PROTAC strategy for inducing DNMT1 degradation, demonstrated the therapeutic potential of DNMT1-targeted PROTACs. This work also provided a convenient chemical knockdown tool for DNMT1-related studies.

The value of a nomogram model based on CT imaging features in differentiating duodenal gastrointestinal stromal tumors from pancreatic head neuroendocrine tumors.

OBJECTIVE: To construct a nomogram model based on multi-slice spiral CT imaging features to predict and differentiate between duodenal gastrointestinal stromal tumors (GISTs) and pancreatic head neuroendocrine tumors (NENs), providing imaging evidence for clinical treatment decisions. METHODS: A retrospective collection of clinical information, pathological results, and imaging data was conducted on 115 cases of duodenal GISTs and 76 cases of pancreatic head NENs confirmed by surgical pathology at Zhongshan Hospital Fudan University from November 2013 to November 2022. Comparative analysis was performed on the tumor's maximum diameter, shortest diameter, long diameter/short diameter ratio, tumor morphology, tumor border, central position of the lesion, lesion long-axis direction, the relationship between tumor and common bile duct (CBD), duodenal side ulceration of the lesion, calcification, cystic and solid proportion within the tumor, thickened feeding arteries, tumor neovascularization, distant metastasis, and CT values during plain and enhanced scans in arterial and venous phases. Statistical analysis was conducted using t-tests, Mann-Whitney U tests, and χ2 tests. Univariate and multivariate logistic regression analyses were used to identify independent predictors for differentiating duodenal GISTs from pancreatic head NENs. Based on these independent predictors, a nomogram model was constructed, and the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. The nomogram was validated using a calibration curve, and decision curve analysis was applied to assess the clinical application value of the nomogram. RESULTS: There were significant differences in the duodenal GISTs group and the pancreatic head NENs group in terms of longest diameter (P < 0.001), shortest diameter (P < 0.001), plain CT value (P < 0.001), arterial phase CT value (P < 0.001), venous phase CT value (P = 0.002), lesion long-axis direction (P < 0.001), central position of the lesion (P < 0.001), the relationship between tumor and CBD(< 0.001), border (P = 0.004), calcification (P = 0.017), and distant metastasis (P = 0.018). Multivariate logistic regression analysis identified uncertain location (OR 0.040, 95% CI 0.003-0.549), near the duodenum (OR 0, 95% CI 0-0.009), with the lesion long-axis direction along the pancreas as a reference, along the duodenum (OR 0.106, 95% CI 0.010-1.156) or no significant difference (OR 4.946, 95% CI 0.453-54.017), and the relationship between tumor and CBD (OR 0.013, 95% CI 0.001-0.180), shortest diameter (OR 0.705, 95% CI 0.546-0.909), and calcification (OR 18.638, 95% CI 1.316-263.878) as independent risk factors for differentiating between duodenal GISTs and pancreatic head NENs (all P values < 0.05). The combined diagnostic model's AUC values based on central position of the lesion, calcification, lesion long axis orientation, the relationship between tumor and CBD, shortest diameter, and the joint diagnostic model were 0.937 (0.902-0.972), 0.700(0.624-0.776), 0.717(0.631-0.802), 0.559 (0.473-0.644), 0.680 (0.603-0.758), and 0.991(0.982-0.999), respectively, with a sensitivity of 97.3% and a specificity of 93.0% for the joint diagnostic model. The nomogram model's AUC value was 0.985(0.973-0.996), with a sensitivity and specificity of 94.7% and 93.9%, respectively. The calibration curve indicated good agreement between predicted and actual risks. Decision curve analysis verified the clinical application value of the nomogram. CONCLUSION: The nomogram model based on CT imaging features effectively differentiates between duodenal GISTs and pancreatic head NENs, aiding in more precise clinical treatment decisions.

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil.

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Integrated examination of the transcriptome and metabolome of the gene expression response and metabolite accumulation in soybean seeds for seed storability under aging stress.

Soybean quality and production are determined by seed viability. A seed's capacity to sustain germination via dry storage is known as its seed life. Thus, one of the main objectives for breeders is to preserve genetic variety and gather germplasm resources. However, seed quality and germplasm preservation have become significant obstacles. In this study, four artificially simulated aging treatment groups were set for 0, 24, 72, and 120 hours. Following an aging stress treatment, the transcriptome and metabolome data were compared in two soybean lines with notable differences in seed vigor-R31 (aging sensitive) and R80 (aging tolerant). The results showed that 83 (38 upregulated and 45 downregulated), 30 (19 upregulated and 11 downregulated), 90 (52 upregulated and 38 downregulated), and 54 (25 upregulated and 29 downregulated) DEGs were differentially expressed, respectively. A total of 62 (29 upregulated and 33 downregulated), 94 (49 upregulated and 45 downregulated), 91 (53 upregulated and 38 downregulated), and 135 (111 upregulated and 24 downregulated) differential metabolites accumulated. Combining the results of transcriptome and metabolome investigations demonstrated that the difference between R31 and R80 responses to aging stress was caused by genes related to phenylpropanoid metabolism pathway, which is linked to the seed metabolite caffeic acid. According to this study's preliminary findings, the aging-resistant line accumulated more caffeic acid than the aging-sensitive line, which improved its capacity to block lipoxygenase (LOX) activity. An enzyme activity inhibition test was used to demonstrate the effect of caffeic acid. After soaking seeds in 1 mM caffeic acid (a LOX inhibitor) for 6 hours and artificially aging them for 24 hours, the germination rates of the R31 and R80 seeds were enhanced. In conclusion, caffeic acid has been shown to partially mitigate the negative effects of soybean seed aging stress and to improve seed vitality. This finding should serve as a theoretical foundation for future research on the aging mechanism of soybean seeds.

Lenvatinib targets STAT-1 to enhance the M1 polarization of TAMs during hepatocellular carcinoma progression.

Lenvatinib, a multitarget kinase inhibitor, has been proven to be effective in the treatment of advanced hepatocellular carcinoma. It has been previously demonstrated that tumour associated macrophages (TAMs) in tumour tissues can promote HCC growth, invasion and metastasis. Furthermore, lenvatinib has certain immunomodulatory effects on the treatment of HCC. However, the role of lenvatinib in macrophage polarization during HCC treatment has not been fully explored. In this study, we used a variety of experimental methods both in vitro and in vivo to investigate the effect of lenvatinib on TAMs during HCC progression. This study is the first to show that lenvatinib can alter macrophage polarization in both humans and mice. Moreover, macrophages treated with lenvatinib in vitro displayed enhanced classically activated macrophages (M1) activity and suppressed liver cancer cell proliferation, invasion, and migration. Furthermore, during the progression of M1 macrophage polarization induced by lenvatinib, STAT-1 was the main target transcription factor, and inhibiting STAT-1 activity reversed the effect of lenvatinib. Overall, the present study provides a theoretical basis for the immunomodulatory function of lenvatinib in the treatment of HCC.

Discovery of Preclinical Candidate AD1058 as a Highly Potent, Selective, and Brain-Penetrant ATR Inhibitor for the Treatment of Advanced Malignancies.

The ataxia telangiectasia-mutated and Rad3-related protein (ATR) plays a crucial role in regulating the cellular DNA-damage response (DDR), making it a promising target for antitumor drug development through synthetic lethality. In this study, we present the discovery and detailed characterization of AD1058, a highly potent and selective ATR inhibitor, with good preclinical pharmacokinetic profiles. AD1058 exhibits superior efficacy in inhibiting cell proliferation, disrupting the cell cycle, and inducing apoptosis compared to AZD6738. AD1058 displays potent antitumor effects as a single agent or in combination with clinically approved tumor therapies such as PARP inhibitors, ionizing radiotherapy, or chemotherapy in vivo. Considering its enhanced ability to permeate the blood-brain barrier, AD1058 is a promising clinical candidate for the treatment of brain metastases and leptomeningeal metastases in solid tumors. Additionally, among reported ATR inhibitors, AD1058 features the shortest synthesis route and the highest efficiency to date.

Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.

Computer aided diagnosis methods play an important role in Attention Deficit Hyperactivity Disorder (ADHD) identification. Dynamic functional connectivity (dFC) analysis has been widely used for ADHD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), which can help capture abnormalities of brain activity. However, most existing dFC-based methods only focus on dependencies between two adjacent timestamps, ignoring global dynamic evolution patterns. Furthermore, the majority of these methods fail to adaptively learn dFCs. In this paper, we propose an adaptive spatial-temporal neural network (ASTNet) comprising three modules for ADHD identification based on rs-fMRI time series. Specifically, we first partition rs-fMRI time series into multiple segments using non-overlapping sliding windows. Then, adaptive functional connectivity generation (AFCG) is used to model spatial relationships among regions-of-interest (ROIs) with adaptive dFCs as input. Finally, we employ a temporal dependency mining (TDM) module which combines local and global branches to capture global temporal dependencies from the spatially-dependent pattern sequences. Experimental results on the ADHD-200 dataset demonstrate the superiority of the proposed ASTNet over competing approaches in automated ADHD classification.

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer.

Objectives: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image. Methods: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Results: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686. Conclusion: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

Gasdermin E benefits CD8+T cell mediated anti-immunity through mitochondrial damage to activate cGAS-STING-interferonß axis in colorectal cancer.

BACKGROUND: Pyroptosis belongs to a unique type of programmed cell death among which GSDME is reported to exert anti-tumor immunity. However, the underlying mechanisms of how to boost tumor-infiltrating lymphocytes and whether it could benefit the efficacy of ICIs are still unknown. METHODS: CRC samples were used to analyze its relationship with CD8+T cells. GSDME in mouse CRC cell lines CT26/MC38 was overexpressed. The infiltration of CD8+T cells in grafted tumors was determined by multiplex flow cytometric analysis and immunohistochemistry. Transcriptomic analysis was performed in cell lines to define key signatures related to its overexpression. The mechanism of how mtDNA was released by GSDME-induced mitochondrial damage and activated cGAS-STING pathway was observed. Whether GSDME benefited ICIs and the relationships with the genotypes of CRC patients were investigated. RESULTS: It had favorable prognostic value in CRC and was positively associated with increased number and functionality of CD8+T cells both in human samples and animal models. This was due to mitochondrial damage and activation of cGAS-STING-IFNß pathway for the recruitment of CD8+T cells. Mechanically, GSDME overexpression enhanced N-GSDME level, leading to the mitochondrial damage and mtDNA was released into cytosol. Finally, GSDME benefited with ICIs and exhibited positive relationships with MSI in CRC patients. CONCLUSION: We presented the mechanism of GSDME in anti-tumor immunity through activating cGAS-STING-IFNß axis mediated by mitochondrial damage, leading to more infiltration of CD8+T cells with synergistic efficacy with ICIs.

Association of Lipoprotein(a) Levels With Myocardial Infarction in Patients With Low-Attenuation Plaque.

BACKGROUND: Lipoprotein(a) (Lp[a]) is associated with an increased risk of myocardial infarction (MI). However, the mechanism underlying this association has yet to be fully elucidated. OBJECTIVES: This multicenter study aimed to investigate whether association between Lp(a) and MI risk is reinforced by the presence of low-attenuation plaque (LAP) identified by coronary computed tomography angiography (CCTA). METHODS: In a derivation cohort, a total of 5,607 patients with stable chest pain suspected of coronary artery disease who underwent CCTA and Lp(a) measurement were prospectively enrolled. In validation cohort, 1,122 patients were retrospectively collected during the same period. High Lp(a) was defined as Lp(a) ≥50 mg/dL. The primary endpoint was a composite of time to fatal or nonfatal MI. Associations were estimated using multivariable Cox proportional hazard models. RESULTS: During a median follow-up of 8.2 years (Q1-Q3: 7.2-9.3 years), the elevated Lp(a) levels were associated with MI risk (adjusted HR [aHR]: 1.91; 95% CI: 1.46-2.49; P < 0.001). There was a significant interaction between Lp(a) and LAP (Pinteraction <0.001) in relation to MI risk. When stratified by the presence or absence of LAP, Lp(a) was associated with MI in patients with LAP (aHR: 3.03; 95% CI: 1.92-4.76; P < 0.001). Mediation analysis revealed that LAP mediated 73.3% (P < 0.001) for the relationship between Lp(a) and MI. The principal findings remained unchanged in the validation cohort. CONCLUSIONS: Elevated Lp(a) augmented the risk of MI during 8 years of follow-up, especially in patients with LAP identified by CCTA. The presence of LAP could reinforce the relationship between Lp(a) and future MI occurrence.

Controlled Size Characterization Process for In-Situ TiB2 Particles from Al Matrix Composites Using Nanoparticle Size Analysis.

The wide size range and high tendency to agglomerate of in-situ TiB2 particles in reinforced Al matrix composites introduce great difficulties in their size characterization. In order to use a nanoparticle size analyzer (NSA) to obtain the precise size distribution of TiB2 particles, a controlled size characterization process has been explored. First, the extraction and drying processes for TiB2 particles were optimized. In the extraction process, alternated applications of magnetic stirring and normal ultrasound treatments were proven to accelerate the dissolution of the Al matrix in HCl solution. Furthermore, freeze-drying was found to minimize the agglomeration tendency among TiB2 particles, facilitating the acquisition of pure powders. Such powders were quantitatively made into an initial TiB2 suspension. Second, the chemical and physical dispersion technologies involved in initial TiB2 suspension were put into focus. Chemically, adding PEI (M.W. 10000) at a ratio of mPEI/mTiB2 = 1/30 into the initial suspension can greatly improve the degree of TiB2 dispersion. Physically, the optimum duration for high-energy ultrasound application to achieve TiB2 dispersion was 10 min. Overall, the corresponding underlying dispersion mechanisms were discussed in detail. With the combination of these chemical and physical dispersion specifications for TiB2 suspension, the bimodal size distribution of TiB2 was able to be characterized by NSA for the first time, and its number-average diameter was 111 ± 6 nm, which was reduced by 59.8% over the initial suspension. Indeed, the small-sized and large-sized peaks of the TiB2 particles characterized by NSA mostly match the results obtained from transmission electron microscopy and scanning electron microscopy, respectively.

Contrastive learning with token projection for Omicron pneumonia identification from few-shot chest CT images.

Introduction: Deep learning-based methods can promote and save critical time for the diagnosis of pneumonia from computed tomography (CT) images of the chest, where the methods usually rely on large amounts of labeled data to learn good visual representations. However, medical images are difficult to obtain and need to be labeled by professional radiologists. Methods: To address this issue, a novel contrastive learning model with token projection, namely CoTP, is proposed for improving the diagnostic quality of few-shot chest CT images. Specifically, (1) we utilize solely unlabeled data for fitting CoTP, along with a small number of labeled samples for fine-tuning, (2) we present a new Omicron dataset and modify the data augmentation strategy, i.e., random Poisson noise perturbation for the CT interpretation task, and (3) token projection is utilized to further improve the quality of the global visual representations. Results: The ResNet50 pre-trained by CoTP attained accuracy (ACC) of 92.35%, sensitivity (SEN) of 92.96%, precision (PRE) of 91.54%, and the area under the receiver-operating characteristics curve (AUC) of 98.90% on the presented Omicron dataset. On the contrary, the ResNet50 without pre-training achieved ACC, SEN, PRE, and AUC of 77.61, 77.90, 76.69, and 85.66%, respectively. Conclusion: Extensive experiments reveal that a model pre-trained by CoTP greatly outperforms that without pre-training. The CoTP can improve the efficacy of diagnosis and reduce the heavy workload of radiologists for screening of Omicron pneumonia.

Ticagrelor alone versus ticagrelor plus aspirin from month 1 to month 12 after percutaneous coronary intervention in patients with acute coronary syndromes (ULTIMATE-DAPT): a randomised, placebo-controlled, double-blind clinical trial.

BACKGROUND: Following percutaneous coronary intervention with stent placement to treat acute coronary syndromes, international clinical guidelines generally recommend dual antiplatelet therapy with aspirin plus a P2Y12 receptor inhibitor for 12 months to prevent myocardial infarction and stent thrombosis. However, data on single antiplatelet therapy with a potent P2Y12 inhibitor earlier than 12 months after percutaneous coronary intervention for patients with an acute coronary syndrome are scarce. The aim of this trial was to assess whether the use of ticagrelor alone, compared with ticagrelor plus aspirin, could reduce the incidence of clinically relevant bleeding events without an accompanying increase in major adverse cardiovascular or cerebrovascular events (MACCE). METHODS: In this randomised, placebo-controlled, double-blind clinical trial, patients aged 18 years or older with an acute coronary syndrome who completed the IVUS-ACS study and who had no major ischaemic or bleeding events after 1-month treatment with dual antiplatelet therapy were randomly assigned to receive oral ticagrelor (90 mg twice daily) plus oral aspirin (100 mg once daily) or oral ticagrelor (90 mg twice daily) plus a matching oral placebo, beginning 1 month and ending at 12 months after percutaneous coronary intervention (11 months in total). Recruitment took place at 58 centres in China, Italy, Pakistan, and the UK. Patients were required to remain event-free for 1 month on dual antiplatelet therapy following percutaneous coronary intervention with contemporary drug-eluting stents. Randomisation was done using a web-based system, stratified by acute coronary syndrome type, diabetes, IVUS-ACS randomisation, and site, using dynamic minimisation. The primary superiority endpoint was clinically relevant bleeding (Bleeding Academic Research Consortium [known as BARC] types 2, 3, or 5). The primary non-inferiority endpoint was MACCE (defined as the composite of cardiac death, myocardial infarction, ischaemic stroke, definite stent thrombosis, or clinically driven target vessel revascularisation), with an expected event rate of 6·2% in the ticagrelor plus aspirin group and an absolute non-inferiority margin of 2·5 percentage points between 1 month and 12 months after percutaneous coronary intervention. The two co-primary endpoints were tested sequentially; the primary superiority endpoint had to be met for hypothesis testing of the MACCE outcome to proceed. All principal analyses were assessed in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, NCT03971500, and is completed. FINDINGS: Between Sept 21, 2019, and Oct 27, 2022, 3400 (97·0%) of the 3505 participants in the IVUS-ACS study were randomly assigned (1700 patients to ticagrelor plus aspirin and 1700 patients to ticagrelor plus placebo). 12-month follow-up was completed by 3399 (>99·9%) patients. Between month 1 and month 12 after percutaneous coronary intervention, clinically relevant bleeding occurred in 35 patients (2·1%) in the ticagrelor plus placebo group and in 78 patients (4·6%) in the ticagrelor plus aspirin group (hazard ratio [HR] 0·45 [95% CI 0·30 to 0·66]; p<0·0001). MACCE occurred in 61 patients (3·6%) in the ticagrelor plus placebo group and in 63 patients (3·7%) in the ticagrelor plus aspirin group (absolute difference -0·1% [95% CI -1·4% to 1·2%]; HR 0·98 [95% CI 0·69 to 1·39]; pnon-inferiority<0·0001, psuperiority=0·89). INTERPRETATION: In patients with an acute coronary syndrome who had percutaneous coronary intervention with contemporary drug-eluting stents and remained event-free for 1 month on dual antiplatelet therapy, treatment with ticagrelor alone between month 1 and month 12 after the intervention resulted in a lower rate of clinically relevant bleeding and a similar rate of MACCE compared with ticagrelor plus aspirin. Along with the results from previous studies, these findings show that most patients in this population can benefit from superior clinical outcomes with aspirin discontinuation and maintenance on ticagrelor monotherapy after 1 month of dual antiplatelet therapy. FUNDING: The Chinese Society of Cardiology, the National Natural Scientific Foundation of China, and the Jiangsu Provincial & Nanjing Municipal Clinical Trial Project. TRANSLATION: For the Mandarin translation of the abstract see Supplementary Materials section.