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ObjectiveTo explore the cytotoxicity of four wild mushrooms involved in a case of Yunnan sudden unexplained death （YNSUD）, to provide the experimental basis for prevention and treatment of YNSUD.MethodsFour kinds of wild mushrooms that were eaten by family members in this YNSUD incident were collected and identified by expert identification and gene sequencing. Raw extracts from four wild mushrooms were extracted by ultrasonic extraction to intervene HEK293 cells, and the mushrooms with obvious cytotoxicity were screened by Cell Counting Kit-8 （CCK-8）. The selected wild mushrooms were prepared into three kinds of extracts, which were raw, boiled, and boiled followed by enzymolysis. HEK293 cells were intervened with these three extracts at different concentrations. The cytotoxicity was detected by CCK-8 combined with lactate dehydrogenase （LDH） Assay Kit, and the morphological changes of HEK293 cells were observed under an inverted phase contrast microscope.ResultsSpecies identification indicated that the four wild mushrooms were Butyriboletus roseoflavus, Boletus edulis, Russula virescens and Amanita manginiana. Cytotoxicity was found only in Amanita manginiana. The raw extracts showed cytotoxicity at the mass concentration of 0.1 mg/mL, while the boiled extracts and the boiled followed by enzymolysis extracts showed obvious cytotoxicity at the mass concentration of 0.4 mg/mL and 0.7 mg/mL, respectively. In addition to the obvious decrease in the number of HEK293 cells, the number of synapses increased and the refraction of HEK293 cells was poor after the intervention of Amanita manginiana extracts.ConclusionThe extracts of Amanita manginiana involved in this YNSUD case has obvious cytotoxicity, and some of its toxicity can be reduced by boiled and enzymolysis, but cannot be completely detoxicated. Therefore, the consumption of Amanita manginiana is potentially dangerous, and it may be one of the causes of the YNSUD.

To improve the accuracy of computer aided diagnosis (CAD) based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in the differentiation of benign and malignant breast lesions, this study proposed a convolutional neural network model (AC_Ulsam_CNN) that is based on multi-modal feature fusion and the combination of asymmetric convolution (AC) and ultra-lightweight subspace attention module (Ulsam). Firstly, the transfer learning method was used to pre-train the model to screen out the most effective DCE-MRI time phase scan for differentiating benign and malignant breast lesions. Then, a network model based on AC_Ulsam_CNN was constructed based on the optimal time phase scan images to enhance the feature expression ability and robustness of the classification model. Finally, multimodal information such as breast imaging reporting and data system (BI-RADS) classification, apparent diffusion coefficient (ADC) and time-signal intensity curve (TIC) type were incorporated for feature fusion, to further improve the distinguishing performance of benign and malignant breast lesions. The performance of the model was verified by 5-fold cross-validation method, and the accuracy (ACC) of the proposed method was 0.826 and the area under the curve (AUC) was 0.877. The experimental results show that the proposed algorithm performs well in the classification of benign and malignant breast lesions with small sample size, and the fusion model based on multimodal data further enriches the feature information, thus this study improves the detection accuracy of lesions, and provides a new method for automatic differential diagnosis of benign and malignant breast lesions.

Background: Iron deficiency is prevalent in patients with chronic kidney disease (CKD), even in those without anemia. However, the effects of iron deficiency on CKD progression and all-cause mortality in non-dialysis-dependent CKD (NDD-CKD) patients without anemia remain incompletely understood. Methods: This multicenter retrospective nationwide cohort study included adult patients with non-anemia NDD-CKD from 24 hospitals across China. The study investigated the associations between serum ferritin or transferrin saturation (TSAT) levels and the risks of CKD progression and all-cause mortality. Results: Among 18,878 patients with NDD-CKD, 9,989 patients were included in the kidney outcome analysis, and 18,481 patients in the all-cause mortality analysis. Of the patients with the measurement, 2,450 (27.2%) had ferritin levels ≤ 100ng/mL and 2,440 (13.1%) had a TSAT level ≤ 20%. Compared with patients with TSAT level of > 20%, those with TSAT level of ≤ 20% had significantly higher risks of CKD progression (adjusted hazard ratio [aHR]: 1.66, 95% confidence intervals [CI]: 1.16–2.37; P = 0.005) and all-cause mortality (aHR: 2.21, 95% CI: 1.36–3.57; P = 0.001). The robustness of results was supported by subgroup analyses. However, there was no significant association found between ferritin levels and the risk of CKD progression or all-cause mortality (P > 0.05). Conclusion: Iron deficiency was prevalent in NDD-CKD patients without anemia, and TSAT could be a modifiable risk factor of CKD progression and all-cause mortality. The screening of iron biomarkers, especially TSAT, in the early stage of NDD-CKD is important to assess and improve prognosis. [ABSTRACT FROM AUTHOR]

Background: The effects of acute kidney injury (AKI) on liver-related outcomes in patients with hepatitis B virus (HBV) infection remain unclear. The study aimed to evaluate the association between AKI with liver-related mortality and complications in patients with HBV infection. Methods: The multicenter, retrospective cohort study included Chinese adults with HBV infection from 24 regional central hospitals between January 2000 and December 2022. AKI was defined as a ≥ 26.5 μmol/l increase in serum creatinine concentration within 48 h, or a ≥ 50% increase over the baseline within 7 days. The primary outcome was post-discharge liver-related mortality, while the secondary outcome was a composite of new-onset liver cirrhosis and hepatocellular carcinoma. Cox proportional hazard model was employed for analyses. Results: Of the 86,204 inpatients with HBV infection and without liver cancer or cirrhosis at baseline, 4407(5.1%) patients experienced AKI. During a mean follow-up of 4.6 ± 2.4 years, 334 (0.4%) patients died of liver-related events. After adjustment, AKI during hospitalization was significantly associated with a higher risk of liver-related mortality after discharge (adjusted hazard ratio (HR), 1.78; 95% confidence intervals (CI), 1.26–2.51, P = 0.001), especially in those with severe AKI. Similarly, AKI was associated with a higher risk of cirrhosis or new-onset hepatocellular carcinoma (adjusted HR, 1.33; 95%CI, 1.10–1.60, P = 0.004). The association between AKI and liver-related outcomes remained consistent across different subgroups. Conclusions: AKI during hospitalization was associated with substantial increased risk of liver-related mortality and incident liver-related complication. Our findings highlight the importance of monitoring AKI in patients with HBV infection for tailoring personalized treatments. [ABSTRACT FROM AUTHOR]

Background: Diabetic kidney disease (DKD) is a significant contributor to the development of end-stage renal disease and cardiovascular disease (CVD), with inflammation being a critical factor in its pathogenesis. The aim of this study is to examine the relationship between the neutrophil-to-lymphocyte ratio (NLR), a new inflammatory marker, and mortality from all causes and CVD in patients with DKD. Methods: This multicenter, retrospective cohort study utilized data from the China Renal Data System (CRDS) on patients with DKD hospitalized between January 1, 2000, and February 28, 2023. The patients' demographic information, along with their initial clinical and laboratory results, were collected and recorded. Follow-up continued until July 1, 2023, and patients were categorized into two groups based on the median baseline NLR. The Cox proportional hazards regression, Restricted cubic spline (RCS) curves, The Kaplan–Meier curve, Fine–Gray competing risk model, Time-dependent ROC and subgroup analysis were used to analyze the association between all-cause mortality and CVD mortality in patients having DKD with varying NLR. Results: This study included 11,427 patients who had been clinically diagnosed with DKD. Baseline NLR was associated with C-reactive protein, procalcitonin, high-sensitivity C-reactive protein, plasma D-dimer, cystatin C, creatinine, urea nitrogen, brain natriuretic peptide, and eGFR. We selected the demographic characteristics, differential factors from univariate analysis, and clinically DKD-related laboratory indicators as covariates for Cox analysis. Results indicated that NLR was an independent risk factor for both all-cause and CVD mortality after adjusting for the relevant variables. The risk of all-cause death and CVD death in the high NLR group was 4.688 and 2.141 times higher, respectively, compared to the low NLR group (HR = 4.688, 95% CI 1.153– 19.061, P = 0.031; HR = 2.141, 95% CI 1.257– 3.644, P = 0.005). However, potential confounding factors and biases, such as unmeasured variables and the influence of treatment interventions, could not be fully accounted for. Conclusion: NLR can independently predict the risk of all-cause and CVD mortality in patients with DKD. Identifying individuals with a high NLR and providing further intervention could be crucial measures to reduce both all-cause and CVD mortality. However, the results should be interpreted with caution due to the study's limitations. [ABSTRACT FROM AUTHOR]

ObjectiveTo explore the possible mechanism of Yunaconitine poisoning by studying the changes of urine metabolic profile in rats chronically poisoned by Yunaconitine via non-targeted metabolomics.MethodsA rat model of Yunaconitine poisoning was established, and a metabolomics method based on UPLC-QTOF-MS technology was used to obtain the urine metabolic profile. Principal component analysis （PCA）, orthogonal projections to latent structures-discriminant analysis （OPLS-DA）, variable importance in projection （VIP） value greater than 1, fold change （FC） value greater than 3 or less than 0.33 and P value less than 0.05 were used to screen potential biomarkers related to the toxicity of Yunaconitine. The metabolic pathway analysis was performed through the MetaboAnalyst website and pathological changes of related tissues were observed.ResultsSixteen potential biomarkers including L-isoleucine were screened, which mainly involved six metabolic pathways including the biosynthesis and degradation of valine, leucine and isoleucine, pentose and glucuronate interconversions, and propanoate metabolism, alanine, aspartate and glutamate metabolism, tyrosine metabolism. Pathological studies showed that rat toxic change in nervous system, liver and cardiac caused by Yunaconitine.ConclusionYunaconitine may cause neurotoxicity, hepatotoxicity and cardiotoxicity by affecting amino acid and glucose metabolism.

Background Cardiorenal syndrome highlights the bidirectional relationship between kidney and heart dysfunction. N-terminal pro-B-type natriuretic peptide (NT-proBNP), which is the gold standard biomarker in heart failure (HF), may be an important biomarker for chronic kidney disease (CKD) progression. However, NT-proBNP is negatively related with estimated glomerular filtration rate (eGFR). In this study, we investigated the association of NT-proBNP, eGFR, and progression of kidney disease in CKD patients without HF. Methods This multicentric retrospective cohort study recruited 23 860 CKD patients without HF, who had at least one NT-proBNP record from China Renal Data System database. Linear regression model evaluated the relationship between eGFR and NT-proBNP. Cox regression analysis assessed the association between NT-proBNP and CKD progression. Sensitivity analysis examined the robustness of the main findings. Results This study involved 23 860 CKD patients without HF, distributed across different CKD stages: 10 526 in stages G1-2, 4665 in G3a, 3702 in G3b, 2704 in G4, and 2263 in G5. NT-proBNP was negatively correlated with eGFR, particularly in stages 4–5 CKD. A 15-unit decrease in eGFR was associated with increases in log (NT-proBNP) levels by 1.04-fold, 1.27-fold, 1.29-fold, 1.80-fold, and 3.50-fold for stages 1–2, 3a, 3b, 4, and 5, respectively. After excluding patients who developed CKD progression within 1 year, the Cox regression analysis revealed that the relationship between NT-proBNP and CKD progression was not significant in stages 4 and 5. However, for stages 1–3, each standard deviation increase in log (NT-proBNP) was associated with a 26%, 36%, and 28% higher risk of CKD progression, with P interaction ≤.001. The hazard ratios were 1.26 (95% confidence intervals (CI), 1.18 to 1.35), 1.36 (95% CI, 1.22 to 1.51), and 1.28 (95% CI, 1.14 to 1.43) for stages 1–2, stage 3a, and stage 3b, respectively. Conclusions Despite its strong inverse association with eGFR, NT-proBNP was positively associated with the risk of progression of kidney disease in CKD patients with stages 1–3 without HF. Future studies should investigate the effectiveness of NT-proBNP as a predictive biomarker for the progression of kidney disease across diverse racial groups and healthcare settings. [ABSTRACT FROM AUTHOR]

Background The diagnosis of acute myocardial infarction (AMI) using high-sensitivity cardiac troponin T (hs-cTnT) remains challenging in patients with kidney dysfunction. Methods In this large, multicenter cohort study, a total of 20 912 adults who underwent coronary angiography were included. Kidney function–specific cut-off values of hs-cTnT were determined to improve the specificity without sacrificing sensitivity, as compared with that using traditional cut-off value (14 ng/L) in the normal kidney function group. The diagnostic accuracy of the novel cut-off values was validated in an independent validation cohort. Results In the derivation cohort (n  = 12 900), 3247 patients had an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Even in the absence of AMI, 50.2% of participants with eGFR <60 mL/min/1.73 m2 had a hs-cTnT concentration ≥14 ng/L. Using 14 ng/L as the threshold of hs-cTnT for diagnosing AMI led to a significantly reduced specificity and positive predictive value in patients with kidney dysfunction, as compared with that in patients with normal kidney function. The kidney function–specific cut-off values were determined as 14, 18 and 48 ng/L for patients with eGFR >60, 60–30 and <30 mL/min/1.73 m2, respectively. Using the novel cut-off values, the specificities for diagnosing AMI in participants with different levels of kidney dysfunction were remarkably improved (from 9.1%–52.7% to 52.8–63.0%), without compromising sensitivity (96.6%–97.9%). Similar improvement of diagnostic accuracy was observed in the validation cohort (n  = 8012). Conclusions The kidney function–specific cut-off values of hs-cTnT may help clinicians to accurately diagnose AMI in patients with kidney dysfunction and avoid the potential overtreatment in practice. [ABSTRACT FROM AUTHOR]

Right ventricle (RV) segmentation is essential for assessing cardiac function in patients with pulmonary hypertension, tetralogy of Fallot, and so on. However, it remains difficult due to the complex structure of heart, thin myocardium and large variability of the RV, as well as the interferences from the fat nearby. Due to its high temporal and spatial resolution, cardiac cine magnetic resonance imaging is widely used for functional evaluation of the heart. This article reviews the commonly-used methods for RV segmentation from the cardiac cine magnetic resonance images. The traditional algorithms are described first, followed by the novel multi-atlas and deep learning methods. Lastly, the evaluation standards for RV segmentation are introduced. It is concluded that the deep learning-based segmentation methods may have the potential to become the method of choice in clinical settings due to their high efficiency and accuracy in the diagnosis and prognosis of the heart-related diseases.

BACKGROUND. Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts. METHODS. We established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively. RESULTS. The AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively. CONCLUSION. Our study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression. FUNDING. National Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC., Introduction Glaucoma is a major chronic eye disease characterized by optic nerve damage and visual field defects (1, 2). Its onset is often insidious, with a risk of irreversible visual [...]

Cardiac right ventricle (RV) segmentation plays an essential role in the functional analysis of heart diseases, such as pulmonary hypertension. The myocardium of RV is thin and irregular-shaped, making the traditional segmentation methods less effective. To improve RV segmentation, a COLLATE (Consensus Level, Labeler Accuracy and Truth Estimation) fusion-based multi-atlas method was developed. The preprocessed target image was first registered to atlas images with a B-spline algorithm optimizing normalized mutual information. The registration coefficients obtained were then used to get a rough RV segmentation for COLLATE fusion. Shape-constrained region growing algorithm was used to correct the segmentation errors. Ten cardiac magnetic resonance datasets were blindly selected to compare the performance of RV segmentation between the method developed and a method based on deep learning. The results of manual segmentation were used as the golden standard. Ejection fraction (EF) calculated with the proposed segmentation method showed better correlation and consistency with the golden standard, relative to the results calculated with the deep learning method.

Compressed sensing is widely used in accelerated magnetic resonance imaging (MRI) to reduce scan time. With compressed sensing, high-quality MR images could be acquired and reconstructed with only a small amount of K space data. The compressed sensing algorithm models image reconstruction as a linear combination minimization problem that includes data fidelity terms, sparse priors, and total variation terms. Sparse representation is a key assumption of the compressed sensing theory, and the quality of reconstruction largely depends on sparse transformation. In this article, we proposed a compressed sensing low-field MRI reconstruction algorithm that combined dual-tree wavelet transform and wavelet tree sparsity. Experimental results demonstrated that the proposed algorithm had certain advantages over the conventional reconstruction algorithm, in terms of certain objective evaluation indicators.

Low-field NMR often uses permanent magnet. The signals obtained contain high level of white Gaussian noises, and have low signal-to-noise ratio (SNR). In recent years, many denoising methods have been proposed for low-field NMR measurements. Most of these methods can remove noises without losing useful information contained in the original signals. Wavelet transform is the most popular denoising method among them. In this paper, we first introduced the theory of wavelet transform analysis, followed by review of three wavelet transform denoising methods for low-field NMR, namely the wavelet threshold method, the wavelet transform modulus maximum method and the correlation of wavelet coefficient method. Finally, we showed that four parameters could be calculated to evaluate the denoising performance.