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The use of Diagnosis-Related Groups (DRG) is a prevalent payment system employed to control hospitalization costs and improve medical efficiency. China has developed an indigenized DRG payment system including Single Disease Payment (SDP), DRGs, and Big Data Diagnosis-Intervention Packet (DIP). In this study, we took cholecystitis as an example, drawing on both primary and secondary data to verify the effectiveness of China’s indigenized DRG system and to introduce the evolution of DRGs in China. Primary data were gathered from Qilu Hospital in 2019–2021. Secondary data were collected from published literature from 2004–2016. Only studies with both pre-SDP/DRG and post-SDP/DRG groups were included. Among the studies included, 92.9% (13/14) reported a significant reduction in hospitalization costs after the implementation of SDP while other studies identified length of stay (LOS) and age as the most significant influential factors in SDP. Furthermore, we elaborated the efficiency of DRGs using data from 2738 inpatients in Qilu hospital. Moreover, 60% (6/10) of the studies from the databases also showed the efficiency of DRGs in different regions. SDP is efficient in saving hospitalization costs, but its implementation is limited. DRGs have a broader scope of application, but their effectiveness remains to be validated. DIP is a brand new concept in China, and more data are needed to assess its efficiency.

Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, complexity, and equation coupling of a general six-axis robotic manipulator pose substantial challenges in solving the IKP precisely and efficiently. To address this issue, we propose a novel approach based on neural network (NN) with numerical error minimization in this paper. Within our framework, the complexity of IKP is first simplified by a strategy called joint space segmentation, with respective training data generated by forward kinematics. Afterwards, a set of multilayer perception networks (MLP) are established to learn from the foregoing data in order to fit the goal function piecewise. To reduce the computational cost of the inference process, a set of classification models is trained to determine the appropriate forgoing MLPs for predictions given a specific input. After the initial solution is sought, being improved with a prediction error minimized, the refined solution is finally achieved. The proposed methodology is validated via simulations on Xarm6—a general 6 degrees of freedom manipulator. Results further verify the feasibility of NN for IKP in general cases, even with a high-precision requirement. The proposed algorithm has showcased enhanced efficiency and accuracy compared to NN-based approaches reported in the literature.

Zearalenone (ZEN), a prevalent mycotoxin contaminating food and known for its intestinal toxicity, has been suggested as a potential risk factor for inflammatory bowel disease (IBD), although the exact relationship between ZEN exposure and IBD remains unclear. In this study, we established a rat model of colon toxicity induced by ZEN exposure to investigate the key targets of ZEN-induced colon toxicity and explore the underlying connection between ZEN exposure and IBD. Histological staining of the rat colon revealed significant pathological changes resulting from ZEN exposure (p < 0.01). Furthermore, the proteomic analysis demonstrated a notable upregulation of protein expression levels, specifically STAT2 (0.12 ± 0.0186), STAT6 (0.36 ± 0.0475) and ISG15 (0.43 ± 0.0226) in the rat colon (p < 0.05). Utilizing bioinformatics analysis, we combined ZEN exposure and IBD clinical sample databases to reveal that ZEN exposure may increase the risk of IBD through activation of the STAT-ISG15 pathway. This study identified novel targets for ZEN-induced intestinal toxicity, providing the basis for further study of ZEN exposure to IBD.

As one of the most important conjugated mycotoxins, zearalenone-14-glucoside (Z14G) has received widespread attention from researchers. Although the metabolism of Z14G in animals has been extensively studied, the intracellular toxicity and metabolic process of Z14G are not fully elucidated. In this study, the cytotoxicity of Z14G to human ovarian granulosa cells (KGN) and the metabolism of Z14G in KGN cells were determined. Furthermore, the experiments of co-administration of β-glucosidase and pre-administered β-glucosidase inhibitor (Conduritol B epoxide, CBE) were used to clarify the mechanism of Z14G toxicity release. Finally, the human colon adenocarcinoma cell (Caco-2) metabolism model was used to verify the toxicity release mechanism of Z14G. The results showed that the IC50 of Z14G for KGN cells was 420 μM, and the relative hydrolysis rate of Z14G on ZEN was 35% (25% extracellular and 10% intracellular in KGN cells). The results indicated that Z14G cannot enter cells, and Z14G is only hydrolyzed extracellularly to its prototype zearalenone (ZEN) by β-glucosidase which can exert toxic effects in cells. In conclusion, this study demonstrated the cytotoxicity of Z14G and clarified the toxicity release mechanism of Z14G. Different from previous findings, our results showed that Z14G cannot enter cells but exerts cytotoxicity through deglycosylation. This study promotes the formulation of a risk assessment and legislation limit for ZEN and its metabolites.

Ziziphi Spinosae Semen (ZSS) has been widely used in traditional Chinese medicine system for decades. Under proper humidity and temperature, ZSS is easily contaminated by fungi and mycotoxins during harvest, storage, and transport, thereby posing a considerable threat to consumer health. In this study, we first used the Illumina MiSeq PE250 platform and targeted the internal transcribed spacer 2 sequences to investigate the presence of fungi in moldy and normal ZSS samples collected from five producing areas in China. Results showed that all 14 samples tested were contaminated by fungi. Ascomycota was the dominant fungus at the phylum level, accounting for 64.36⁻99.74% of the fungal reads. At the genus level, Aspergillus, Candida, and Wallemia were the most predominant genera, with the relative abundances of 13.52⁻87.87%, 0.42⁻64.56%, and 0.06⁻34.31%, respectively. Meanwhile, 70 fungal taxa were identified at the species level. Among these taxa, three potential mycotoxin-producing fungi, namely, Aspergillus flavus, A. fumigatus, and Penicillium citrinum that account for 0.30⁻36.29%, 0.04⁻7.37%, and 0.01⁻0.80% of the fungal reads, respectively, were detected in all ZSS samples. Moreover, significant differences in fungal communities were observed in the moldy and normal ZSS samples. In conclusion, our results indicated that amplicon sequencing is feasible for the detection and analysis of the fungal community in the ZSS samples. This study used a new approach to survey the fungal contamination in herbal materials. This new approach can provide early warning for mycotoxin contamination in herbal materials, thereby ensuring drug efficacy and safety.