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Tetrodotoxin (TTX) is a highly potent and widely distributed ion-channel marine neurotoxin; it has no specific antidote and poses a great risk to human health. Therefore, detecting and quantifying TTX to effectively implement prevention strategies is important for food safety. The development of novel and highly sensitive, highly specific, rapid, and simple techniques for trace TTX detection has attracted widespread attention. This review summarizes the latest advances in the detection and quantitative analysis of TTX, covering detection methods based on biological and cellular sensors, immunoassays and immunosensors, aptamers, and liquid chromatography-mass spectrometry. It further discusses the advantages and applications of various detection technologies developed for TTX and focuses on the frontier areas and development directions of TTX detection, providing relevant information for further investigations.
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Background: In type 2 diabetes mellitus (T2DM) patients, left ventricular systolic dyssynchrony (LVSD) with normal left ventricular ejection fraction (LVEF) and normal myocardial perfusion could referred to as subclinical myocardial damage, which is difficult to diagnose at an early stage. Epicardial adipose tissue, a distinctive heart-specific visceral fat, is closely related to various cardiovascular diseases. The objective of this study was to investigate the correlation between epicardial fat volume (EFV) and subclinical myocardial damage in T2DM patients. Methods: This retrospective cross-sectional study included 117 T2DM patients with normal myocardial perfusion by single photon emission computed tomography-computed tomography (SPECT-CT) and normal LVEF by echocardiography. The study was conducted from January 2018 to December 2022. Patient data were collected through electronic medical records including basic patient information, medical history, laboratory tests, and medication data. The EFV was quantified through a non-contrast CT scan. Quantitative indicators of LVSD including phase standard deviation (PSD) and phase histogram bandwidth (PBW) were obtained through phase analysis of the gated rest myocardial perfusion imaging (MPI). Additionally, 83 healthy individuals at the same time were selected to gain the reference threshold of LVSD indicators (13.1° for PSD and 37.6° for PBW). Univariate and multivariable logistic regression models were performed to analyze factors influencing LVSD. A generalized additive model (GAM) was applied to explore the relationship between EFV and LVSD. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of EFV for LVSD. Results: Among all patients, 32 (27.4%) patients had LVSD. Compared with the non-LVSD group, the body mass index (BMI) and EFV were higher in the LVSD group (25.83±2.66 vs. 23.94±3.13 kg/m2; 142.41±44.17 vs. 108.01±38.24 cm3, respectively, both P<0.05). Multivariate regression analysis revealed that EFV was independently associated with LVSD [odds ratio (OR) =1.19; 95% confidence interval (CI): 1.06-1.34; P=0.003]. Age, BMI, incidence of hypertension, and LVSD were increased with tertiles of EFV (all P<0.05). The GAM indicated a linear association between EFV and LVSD. The ROC curve analysis concluded that the area under the curve (AUC) of EFV for predicting subclinical myocardial damage in T2DM patients was 0.732 (95% CI: 0.633-0.831, P<0.001), with the optimal threshold of 122.26 cm3, sensitivity of 71.9%, and specificity of 69.4%. Conclusions: EFV is an independent risk factor for LVSD in T2DM patients with normal LVEF and normal MPI, which could potentially serve as a novel imaging marker and a potential therapeutic target for subclinical myocardial damage.
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OBJECTIVES: Myocardial damage is the important cause of heart failure (HF) in type 2 diabetes mellitus (T2DM), which is difficult to early diagnose, especially in T2DM with normal left ventricular ejection fraction (LVEF) and normal myocardial perfusion. The goal was to evaluate myocardial damage in T2DM with normal LVEF and normal myocardial perfusion by detecting left ventricular systolic dyssynchrony (LVSD), and find out the risk factors associated with LVSD. METHODS: This study included 95 T2DM with normal LVEF, normal myocardial perfusion. 69 consecutive individuals without T2DM and CAD were enrolled as the control group with age-, sex- and BMI-matched. All participants underwent stress/rest 99mtechnetium-sestamibi (99mTc-MIBI) gated myocardial perfusion imaging (GMPI) and two-dimensional echocardiography within 1 week. Clinical data including age, gender, BMI, duration of diabetes, chronic diabetic complications, glycated haemoglobin A1c (HbA1c), fast blood glucose (FBG) and Brain Natriuretic Peptide (BNP) were collected from medical records. Left ventricular synchrony parameters were acquired, including phase standard deviation (PSD) and phase histogram bandwidth (PBW) by rest GMPI. RESULTS: PSD and PBW in T2DM group were significantly higher than control group (P < .05). LVSD was detected in 20 (21%) T2DM patients. Compared to non-LVSD T2DM group, LVSD T2DM group had higher BMI, higher prevalence of BNP [Formula: see text] 35 pg/mL and chronic diabetic complications (P < .05). BNP [Formula: see text] 35 pg/mL had mild positive association with LVSD (r = 0.318, P = .004). In multivariate logistic regression, chronic diabetic complications and high BMI (> 23.4 kg/m2) were independent risk factors of LVSD (OR 5.64, 95% CI 1.58-20.16, P = .008; OR 6.77, 95% CI 1.59-28.89, P = .010). CONCLUSIONS: LVSD existed in T2DM patients with normal LVEF and normal myocardial perfusion. Chronic diabetic complications and high BMI (> 23.4 kg/m2) were the independent risk factors of LVSD. LVSD based on GMPI can be the novel imaging marker to early assess myocardial damage in T2DM patients.
