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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]

Artificial intelligence (AI)-based multi-modal fusion algorithms are pivotal in emulating clinical practice by integrating data from diverse sources. However, most of the existing multi-modal models focus on designing new modal fusion methods, ignoring critical role of feature representation. Enhancing feature representativeness can address the noise caused by modal heterogeneity at the source, enabling high performance even with small datasets and simple architectures. Here, we introduce DeepOmix-FLEX (Fusion with Learning Enhanced feature representation for X-modal or FLEX in short), a multi-modal fusion model that integrates clinical data, proteomic data, metabolomic data, and pathology images across different scales and modalities, with a focus on advanced feature learning and representation. FLEX contains a Feature Encoding Trainer structure that can train feature encoding, thus achieving fusion of inter-feature and inter-modal. FLEX achieves a mean AUC of 0.887 for prediction of chronic kidney disease progression on an internal dataset, exceeding the mean AUC of 0.727 using conventional clinical variables. Following external validation and interpretability analyses, our model demonstrated favorable generalizability and validity, as well as the ability to exploit markers. In summary, FLEX highlights the potential of AI algorithms to integrate multi-modal data and optimize the allocation of healthcare resources through accurate prediction. [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.