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Breast ultrasound is recommended for early breast cancer detection in China, but the rapid increase in imaging data burdens sonographers. This study evaluated the agreement between artificial intelligence (AI) software and sonographers in analyzing breast nodule features. Breast ultrasound images from two hospitals in Shanghai were analyzed by both the software and the sonographers for features including echotexture, echo pattern, orientation, shape, margin, calcification, and posterior echo attenuation. Agreement between software and sonographers was compared using the proportion of agreement and Kappa, with analysis time also evaluated. A total of 493 images were analyzed. The proportion of agreement between software and sonographers in assessing features was 80.5% for echotexture, 84.4% for echo pattern, 93.7% for orientation, 85.8% for shape, 88.6% for margin, 80.5% for calcification, and 90.5% for posterior echo attenuation, highlighting software’s high accuracy. Cohen’s kappa for other features indicated moderate to substantial agreement (0.411–0.674), with calcification showing fair agreement (0.335). The software significantly reduced analysis time compared to sonographers ( P  < 0.001). The software showed high accuracy and time efficiency. AI software presents a viable solution for reducing sonographers’ workload and enhance healthcare in underserved areas by automating feature analysis in breast ultrasound images.

Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imaging, plays an important role in accurately diagnosing and managing thyroid nodules. However, there is a high degree of inter- and intra-observer variability in image interpretation due to the different knowledge and experience of sonographers who have huge ultrasound examination tasks everyday. Artificial intelligence based on computer-aided diagnosis technology maybe improve the accuracy and time efficiency of thyroid nodules diagnosis. This study introduced an artificial intelligence software called SW-TH01/II to evaluate ultrasound image characteristics of thyroid nodules including echogenicity, shape, border, margin, and calcification. We included 225 ultrasound images from two hospitals in Shanghai, respectively. The sonographers and software performed characteristics analysis on the same group of images. We analyzed the consistency of the two results and used the sonographers’ results as the gold standard to evaluate the accuracy of SW-TH01/II. A total of 449 images were included in the statistical analysis. For the seven indicators, the proportions of agreement between SW-TH01/II and sonographers’ analysis results were all greater than 0.8. For the echogenicity (with very hypoechoic), aspect ratio and margin, the kappa coefficient between the two methods were above 0.75 (P < 0.001). The kappa coefficients of echogenicity (echotexture and echogenicity level), border and calcification between the two methods were above 0.6 (P < 0.001). The median time it takes for software and sonographers to interpret an image were 3 (2, 3) seconds and 26.5 (21.17, 34.33) seconds, respectively, and the difference were statistically significant (z = -18.36, P < 0.001). SW-TH01/II has a high degree of accuracy and great time efficiency benefits in judging the characteristics of thyroid nodule. It can provide more objective results and improve the efficiency of ultrasound examination. SW-TH01/II can be used to assist the sonographers in characterizing the thyroid nodule ultrasound images.

Background Endothelial-to-mesenchymal transition (EndMT) in endothelial dysfunction exacerbates hypertension. However, the regulatory mechanisms underlying EndMT in hypertension are yet to be elucidated. Methods The N-acetyltransferase 10 (NAT10) and N4-acetylcytidine (ac4C) levels were determined in hypertensive mice, spontaneously hypertensive rats (SHRs), and angiotensin II (Ang II)-treated human umbilical vein endothelial cells (HUVECs). Biological functional assays were performed with lentiviral vectors to induce the overexpression or knockdown of NAT10 in vivo and in vitro. The detailed mechanisms underlying the role of ac4C-mediated posttranscriptional regulation in hypertension were investigated by combining ac4C-RIP-seq with RNA-seq, RIP-qRCR, mRNA stability, and dual-luciferase assays. Mitochondrial biogenesis and function were assessed via reactive oxygen species (ROS) and mitochondrial ROS (mtROS) staining; estimation of ATP levels, the mitochondrial membrane potential (MMP), and the mtDNA content; and evaluation of mitochondrial respiratory chain complex activities. Results The results revealed that NAT10 and ac4C levels are higher in the hypertensive mice descending thoracic aorta tissues, SHRs descending thoracic aorta samples, and Ang II-treated HUVECs compared to the control groups. NAT10 overexpression inhibits EndMT in hypertension, which is partly due to the inhibition of endothelial dysfunction, whereas NAT10 inhibition has the opposite effect. Mechanistically, NAT10 inhibited endothelial dysfunction in hypertension through increased AdipoR1 mRNA ac4C acetylation. Moreover, NAT10 induced AdipoR1 expression, leading to increased mitochondrial biogenesis and function in Ang II-treated ECs via p38 MAPK/PGC-1α signaling. Conclusions The current data highlighted the molecular mechanisms of NAT10-induced ac4C acetylation and implied that the NAT10-AdipoR1 axis might be the therapeutic target to inhibit endothelial dysfunction and EndMT in hypertension.

Electronic health literacy (eHL) has been increasingly associated with health-related quality of life (HRQoL). However, the underlying mechanisms, especially in the general population, remain insufficiently explored. This study aimed to investigate the mediating role of health self-management behaviors (HSMB) in the relationship between eHL and HRQoL. A cross-sectional study was conducted in Shanghai, China, from October to December 2022. Participants were recruited via convenience sampling from 7 community health service centers. Data were collected through an online survey platform Wenjuanxing. Validated scales, including the eHL Scale, the adults' health self-management skill rating scale, and the 12-item short form health survey were used to measure eHL, HSMB, and HRQoL, respectively. The HRQoL was summarized into the physical component summary (PCS) and the mental component summary (MCS). Correlation analysis, multivariate linear regression with stepwise backward selection, and mediation analysis were performed to explore the relationships among eHL, HSMB, PCS, and MCS, with adjustments for sociodemographic and health-related covariates. Among the 2364 participants recruited from urban, periurban, and rural areas, eHL scores varied significantly by demographic characteristics. Positive correlations among eHL, HSMB, PCS, and MCS were observed, with Spearman correlation coefficients ranging from 0.24 to 0.46 (P<.001). Multivariate analysis showed that eHL was significantly positively associated with PCS (R2=0.14, 95% CI 0.09-0.18, P<.001) and MCS (R2=0.23, 95% CI 0.17-0.28, P<.001). Mediation analysis indicated that eHL had a significant direct (PCS: βc=.18, 95% CI 0.13-0.23, P<.001; MCS: βc=.32, 95% CI 0.25-0.38, P<.001) and an indirect effect on HRQoL through HSMB (PCS: βc'=.11, 95% CI 0.09-0.14, P<.001; MCS: βc'=.14, 95% CI 0.10-0.17, P<.001). This study demonstrated a positive association between eHL and HRQoL, with HSMB acting as a partial mediator among the general population in Shanghai. Targeted interventions should be implemented to improve eHL and HSMB.

BackgroundGastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies.MethodsBased on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes.ResultsWe identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T–activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well.ConclusionThis study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.

Increasing evidence revealed that apoptosis and oxidative stress injury were associated with the pathophysiology of doxorubicin (DOX)-induced myocardial injury. ELABELA (ELA) is a newly identified peptide with 32 amino acids, can reduce hypertension with exogenous infusion. However, the effect of 11-residue furn-cleaved fragment (ELA-11) is still unclear. We first administrated ELA-11 in DOX-injured mice and measured the cardiac function and investigated the effect of ELA-11 in vivo . We found that ELA-11 alleviated heart injury induced by DOX and inhibited cardiac tissues from apoptosis. In vitro , ELA-11 regulated the sensitivity towards apoptosis induced by oxidative stress with DOX treatment through PI3K/AKT and ERK/MAPK signaling pathway. Similarly, ELA-11 inhibited oxidative stress-induced apoptosis in cobalt chloride (CoCl 2 )-injured cardiomyocytes. Moreover, ELA-11 protected cardiomyocyte by interacting with Apelin receptor (APJ) by using 4-oxo-6-((pyrimidin-2-ylthio) methyl)-4H-pyran-3-yl 4-nitrobenzoate (ML221). Hence, our results indicated a protective role of ELA-11 in oxidative stress-induced apoptosis in DOX-induced myocardial injury.

Doxorubicin (DOX) is a widely used chemotherapeutic agent known for its efficacy against various cancers, but its clinical application is often limited by its cardiotoxic effects. The exact mechanisms of DOX-induced cardiotoxicity remain unclear, requiring further investigation. Early diagnosis is essential to enhance the quality of life and prognosis for patients with malignancies. This study aims to identify biomarkers and therapeutic targets for DOX cardiotoxicity. Heart tissue samples from 20 DOX-treated cardiotoxic mice and 19 normal controls were analyzed using liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analysis identified differential metabolites. Key metabolites were assessed using a random forest algorithm, and ROC curves evaluated diagnostic value. H9C2 rat cardiomyoblast cells were cultured to investigate the protective effects of these metabolites. Among 291 metabolites, significant differences emerged between cardiotoxic and normal mice. Five metabolites-4-hydroxy-valeric acid, 2-methylbutanoic acid, traumatic acid, PI (18:2 (9Z, 12Z)/0:0), and MIPC (t18:0/24:0 (2OH))-showed diagnostic potential. ROC analysis indicated excellent value for 4-hydroxy-valeric acid and PI (18:2 (9Z, 12Z)/0:0) and high discriminatory power for 2-methylbutanoic acid (AUC = 0. 99). Pathway analysis highlighted glycosylphosphatidylinositol-anchor biosynthesis, unsaturated fatty acids biosynthesis, pantothenate and CoA pathways, among others, associated with DOX-induced cardiotoxicity. In addition, we found that the differential metabolite Cer (d18:0/12:0) can improve DOX-induced myocardial cell damage and inhibit apoptosis-related protein expression at the cellular level. Heart tissue metabolomics with LC-MS identified critical metabolites and pathways associated with DOX cardiotoxicity, suggesting biomarkers for early diagnosis and potential therapeutic targets to mitigate DOX-related cardiotoxicity and improve clinical outcomes.

Background Lung cancer remains a major global health threat, with its biological complexity and patient heterogeneity posing significant challenges. Novel machine learning approaches now offer effective tools to interpret complex biological information hierarchies, showing promise to transform lung cancer treatment approaches. Methods We analyzed comprehensive biological datasets from TCGA and other databases, integrating DNA, RNA, miRNA, protein, and metabolite information. Multiple machine learning methods were employed to build diagnostic tools, treatment response predictors, and survival estimation models. Results Our machine learning approaches effectively distinguished cancer patients from healthy controls. Analysis identified unique molecular characteristics between lung cancer subtypes and discovered biomarkers that help predict treatment efficacy and patient prognosis. Adding clinical data to biological information significantly improved model accuracy and enhanced patient stratification. Conclusion This study marks significant progress toward precision cancer therapy by demonstrating how machine learning can help decode the complex biology of lung cancer.

Background The toxicity of the contaminant N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine quinone (6-PPDQ) is a growing concern. However, its potential myocardial toxicity remains unclear. In this study, the impact of 6-PPDQ on myocardial tissue was investigated. Differential RNA expression profiles and metabolic patterns in myocardial tissues from 6-PPDQ–exposed and untreated mice were examined using whole-transcriptome sequencing and untargeted metabolomics based on RNA sequencing and ultra-high-performance liquid chromatography. Results Our results showed that 6-PPDQ induces myocardial damage by exacerbating pathological changes, elevating cardiac injury biomarker levels, and increasing cardiomyocyte apoptosis. Transcriptome analysis revealed differentially expressed long noncoding RNAs (501), circular RNAs (138), microRNAs (63), and mRNAs (4054) in the 6-PPDQ group compared with the controls. Metabolomic analysis revealed 208 differential metabolites, along with dysregulation of ABC transporters, the citrate cycle, and efferocytosis. Integrated whole-transcriptome and untargeted metabolomics profiling presented Myl7 , Orm1 , Usp29 , and Rxfp1 were participated in 6-PPDQ induces myocardial injury. Finally, Myl7 was identified as the potential candidate target of 6-PPDQ through molecular docking and expression analyses, and molecular dynamics (MD) simulations. Conclusions These findings collectively indicate that 6-PPDQ exacerbates cardiac damage partially through the dysregulation of Myl7 expression, highlighting the mechanisms underlying its myocardial toxicity.

To combat/control the COVID-19 pandemic, a complete lockdown was implemented in China for almost 6 months during 2020. To determine the impact of a long-term lockdown on the academic performance of first-year nursing students via mandatory online learning, and to determine the benefits of online teaching. The recruitment and academic performance of 1st-year nursing students were assessed between 2019 [prior to COVID-19, n = 195, (146 women)] and 2020 [during COVID-19, n = 180 (142 women)]. The independent sample t test or Mann-Whitney test was applied for a comparison between these two groups. There was no significant difference in student recruitment between 2019 and 2020. The overall performance of the first-year students improved in the Biochemistry, Immunopathology, Traditional Chinese Medicine Nursing and Combined Nursing courses via mandatory online teaching in 2020 compared with traditional teaching in 2019. Suspension of in-class learning but continuing education virtually online has occurred without negatively impacting academic performance, thus academic goals are more than achievable in a complete lockdown situation. This study offers firm evidence to forge a path for developments in teaching methods to better incorporate virtual learning and technology in order to adapt to fast-changing environments. However, the psychological/psychiatric and physical impact of the COVID-19 lockdown and the lack of face-to-face interaction on these students remains to be explored.