ObjectiveTo investigate the contamination levels of zearalenone (ZEN) in commercially available edible vegetable oils in Ningbo City and to assess its health risks to local residents. MethodsA total of 330 samples of commercially available edible vegetable oil samples (50 each of peanut oil, corn oil, and olive oil; 40 each of rapeseed oil and blended oil; 30 each of soybean oil, rice oil, and sunflower seed oil; and 10 of camellia oil) were collected in 2024. The samples were tested for ZEN using the first method specified in GB 5009.209‒2016 National Food Safety Standard―Determination of Zearalenone in Food, namely the liquid chromatography method, and the contamination status was analyzed. Additionally, combined with dietary consumption data of residents, the Monte Carlo simulation method was employed to evaluate the health risks of ZEN in edible vegetable oils. ResultsZEN was detected in 267 out of 330 samples, with a detection rate of 80.91%, and the median (P₅₀) and the 25th, 75th percentiles (P₂₅, P₇₅) of ZEN concentrations were 2.02 (0.37, 17.90) μg·kg^-1, with a maximum value of 342.00 μg·kg^-1. The ZEN detection rates in corn oil, peanut oil, and blended oil were all 100.00%. The daily average exposure (P₅₀) and daily high exposure (P₉₅) to ZEN via edible vegetable oils among Ningbo residents were 0.001 μg·kg^-1 (normalized to body weight, same below) and 0.060 μg·kg^-1, respectively. However, 1.22% of Ningbo residents had a daily ZEN exposure exceeding the tolerable daily intake (TDI) of 0.25 μg·kg^-1. The hazard quotients (HQ) for the daily average exposure (P₅₀) and daily high exposure (P₉₅) levels were 0.004 and 0.020, respectively, both substantially below 1. Nevertheless, the probability of health risk for Ningbo residents due to ZEN exposure from vegetable oil consumption remained at 1.02%. ConclusionEdible vegetable oils in Ningbo City were contaminated with ZEN, but the probability of ZEN exposure exceeding the TDI through edible vegetable oils was relatively low, and the associated health risk probability were also minimal, indicating an overall insignificant health risk.

Metabolic associated fatty liver disease （MAFLD） is a common chronic liver disease worldwide， and timely and precise intervention can delay disease progression and significantly reduce the risk of serious complications such as liver fibrosis， liver cirrhosis， and liver cancer. Although traditional liver biopsy combined with metabolic markers is the gold standard， it may cause complications such as pain and bleeding as an invasive examination， which has promoted scientific research to shift its focus to the construction of noninvasive assessment systems. In recent years， noninvasive diagnostic technologies based on multi-dimensional detection strategies have been continuously updated， including serological models， imaging techniques， and clinical algorithms. This article systematically reviews the screening methods for MAFLD during the fibrotic stages F1—F3， especially deep learning models based on artificial intelligence， in order to provide ideas for the early screening of MAFLD， as well as a scientific reference for optimizing disease management strategies.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Mitochondria, functioning not only as the central hub of cellular energy metabolism but also as semi-autonomous organelles, orchestrate cellular fate decisions through their endogenous mitochondrial DNA (mtDNA), which encodes core components of the electron transport chain. Emerging research has identified microRNAs localized within mitochondria, termed mitochondria-located microRNAs (mitomiRs). Recent studies have revealed that mitomiRs are transcribed from nuclear DNA (nDNA), processed and matured in the cytoplasm, and subsequently transported into mitochondria. mitomiRs regulate mtDNA through diverse mechanisms, including modulation of mtDNA expression at the translational level and direct binding to mtDNA to influence transcription. Aberrant expression of mitomiRs leads to mitochondrial dysfunction and contributes to the pathogenesis of metabolic diseases. Restoring mitomiR expression to physiological levels using mitomiRs mimics or inhibitors has been shown to improve mitochondrial function and alleviate related diseases. Consequently, the regulatory mechanisms of mitomiRs have become a major focus in mitochondrial research. Given that mitomiRs are located in mitochondria, targeted delivery strategies designed for mtDNA can be adapted for the delivery of mitomiRs mimics or inhibitors. However, numerous intracellular and extracellular barriers remain, highlighting the need for more precise and efficient delivery systems in the future. The regulation of mtDNA expression mediated by mitomiRs not only expands our understanding of miRNA functions in post-transcriptional gene regulation but also provides promising molecular targets for the treatment of mitochondrial-related diseases. This review systematically summarizes recent research progress on mitomiRs in regulating mtDNA expression and discusses the underlying mechanisms of mitomiRs-mtDNA interactions. Additionally, it provides new perspectives on precision therapeutic strategies, with a particular emphasis on mitomiRs-based regulation of mitochondrial function in mitochondrial-related diseases.

Objective To explore the association between oral microbiota and esophageal carcinogenesis. Methods A case-control study design was employed. A total of 309 subjects were recruited, consisting of 159 healthy controls, 32 cases of esophageal basal cell hyperplasia, 32 cases of low-grade intraepithelial neoplasia, 14 cases of high-grade intraepithelial neoplasia, and 72 cases of esophageal squamous cell carcinoma. Tongue swab samples were collected for 16S rRNA sequencing. The α-diversity and β-diversity of the microbiota were analyzed, and the characteristics of the microbial communities at different stages of esophageal carcinogenesis were compared. The strength of the association was expressed by odds ratio (OR) and 95% confidence interval (CI). Results α-diversity analysis indicated significant differences in the observed species number (Sobs) index across various stages of esophageal cancer progression (P<0.001). After adjusting for confounding factors such as age, gender, smoking, and alcohol consumption, the Simpson index was positively correlated with carcinogenesis (P=0.006). β-diversity analysis revealed differences in microbiota structure among the groups. After ordered multinomial logistic regression analysis and adjustment for multiple confounding factors, the relative abundance of Peptostreptococcus (OR: 2.06, 95%CI: 1.22–3.60), Patescibacteria (OR: 1.31, 95%CI: 1.04–1.67), Capnocytophaga (OR: 1.24, 95%CI: 1.05–1.54), and Bacteroidota (OR: 1.02, 95%CI: 1.00–1.05) was positively correlated with carcinogenesis. The relative abundance of Stomatobaculum (OR: 0.57, 95%CI: 0.30–1.00) and Actinobacteriota (OR: 0.95, 95%CI: 0.92–0.98) was negatively correlated with carcinogenesis. Conclusion Specific oral microbiotas are significantly associated with esophageal carcinogenesis, and synergistic or antagonistic interactions may be observed among the microbiota.

A primary hallmark of pathological cardiac hypertrophy is excess protein synthesis due to enhanced translational activity. However, regulatory mechanisms at the translational level under cardiac stress remain poorly understood. Here we examined the translational regulations in a mouse cardiac hypertrophy model induced by transaortic constriction (TAC) and explored the conservative networks versus the translatome pattern in human dilated cardiomyopathy (DCM). The results showed that the heart weight to body weight ratio was significantly elevated, and the ejection fraction and fractional shortening significantly decreased 8 weeks after TAC. Puromycin incorporation assay showed that TAC significantly increased protein synthesis rate in the left ventricle. RNA-seq revealed 1,632 differentially expressed genes showing functional enrichment in pathways including extracellular matrix remodeling, metabolic processes, and signaling cascades associated with pathological cardiomyocyte growth. When combined with ribosome profiling analysis, we revealed that translation efficiency (TE) of 1,495 genes was enhanced, while the TE of 933 genes was inhibited following TAC. In DCM patients, 1,354 genes were upregulated versus 1,213 genes were downregulated at the translation level. Although the majority of the genes were not shared between mouse and human, we identified 93 genes, including Nos3, Kcnj8, Adcy4, Itpr1, Fasn, Scd1, etc., with highly conserved translational regulations. These genes were remarkably associated with myocardial function, signal transduction, and energy metabolism, particularly related to cGMP-PKG signaling and fatty acid metabolism. Motif analysis revealed enriched regulatory elements in the 5' untranslated regions (5'UTRs) of transcripts with differential TE, which exhibited strong cross-species sequence conservation. Our study revealed novel regulatory mechanisms at the translational level in cardiac hypertrophy and identified conserved translation-sensitive targets with potential applications to treat cardiac hypertrophy and heart failure in the clinic.
Animals ; Humans ; Cardiomegaly/physiopathology* ; Ribosomes/physiology* ; Protein Biosynthesis/physiology* ; Mice ; Cardiomyopathy, Dilated/genetics* ; Ribosome Profiling