Both porto-sinusoidal vascular disorder and hepatic sinusoidal obstruction syndrome belong to hepatic vascular diseases， and they have differences in etiology， clinical manifestations， treatment， and prognosis. However， recent research evidence has gradually shown that the association between porto-sinusoidal vascular disorder and hepatic sinusoidal obstruction syndrome has not been fully understood， suggesting that they may be two different manifestations of the same disease. This article reviews the association between these two diseases and related research evidence.

As a neglected tropical disease, schistosomiasis remains a major public health challenge in underdeveloped areas, notably Africa. Currently, the national schistosomiasis control programmes in Africa mainly depend on foreign aids; however, conventional international aid models have multiple limitations. To enhance the effectiveness and sustainability of global schistosomiasis control programmes, this article proposes a trinity collaboration model based on international rules, China’s experiences and local needs, which is explained with China aid project of schistosomiasis control in Zanzibar as an example. Based on the successful experiences from the national schistosomiasis control programme in China, this model emphasizes the compliance with World Health Organization guidelines and fully considers local actual needs to promote the effectiveness and sustainability of the schistosomiasis control programme through integrating international resources and promoting China’s experience to meet local needs. The successful practice of the China aid project of schistosomiasis control in Zanzibar provides strong evidence that the model is of great theoretical significance and practical value to improve the efficiency of multilateral collaboration and promote global health governance.

Our study aims at systematically summarizing and evaluating the applications of digital intelligence technologies in the field of rare disease medical care insurance now and in the future and at constructing a conceptual framework for the digital powered mechanism for the medical care insurance for rare diseases. By using Chinese keywords of " rare disease" " medical insurance"" artificial intelligence"" prediction model"" machine learning"" big data"" algorithm" and their English equivalents, we searched the databases of PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, collected relevant literature, and decided the criteria of inclusion and exclusion. The finding of our study shows that medical care insurance mechanism of rare disease in China faces significant challenges in drug accessbility and the funding sustainability. Meanwhile, our study shows that the digital intelligence technologies have broad potential in applications-in financing, accessbility, payment, and supervision. Specifically, dynamic simulation models and big data analysis can make precise prediction of the demand for funding of medical care insurance. The machine learning algorithms improve the dynamic evaluation of drug safety and cost-effectiveness. The personalized payment models enhance the efficiency in identifying the cohort with high expenditure so as to alleviate fund expenditure pressures. The intelligent monitoring technologies can accurately detect the abnormal behaviors in funds of medical care insurance. These technologies provide systematic and scientific solutions for improving the medical care mechanism for rare diseases. Even though further investigation is needed, the digital intelligence technologies have shown remarkable potential in enhancing the flexibility, efficiency, and sustainability of the medical care insurance system and a promising future in meeting the needs of patients with rare diseases.

Social working memory (SWM)-the ability to maintain and manipulate social information in the brain-plays a crucial role in social interactions. However, research on SWM is still in its infancy and is often treated as a unitary construct. In the present study, we propose that SWM can be conceptualized as having two relatively independent components: "externally oriented SWM" (e-SWM) and "internally oriented SWM" (i-SWM). To test this external-internal hypothesis, participants were tasked with memorizing and ranking either facial expressions (e-SWM) or personality traits (i-SWM) associated with images of faces. We then examined the neural correlates of these two SWM components and their functional roles in empathy. The results showed distinct activations as the e-SWM task activated the postcentral and precentral gyri while the i-SWM task activated the precuneus/posterior cingulate cortex and superior frontal gyrus. Distinct multivariate activation patterns were also found within the dorsal medial prefrontal cortex in the two tasks. Moreover, partial least squares analyses combining brain activation and individual differences in empathy showed that e-SWM and i-SWM brain activities were mainly correlated with affective empathy and cognitive empathy, respectively. These findings implicate distinct brain processes as well as functional roles of the two types of SWM, providing support for the internal-external hypothesis of SWM.
Humans ; Memory, Short-Term/physiology* ; Male ; Female ; Empathy/physiology* ; Young Adult ; Magnetic Resonance Imaging ; Adult ; Brain/diagnostic imaging* ; Brain Mapping ; Facial Expression ; Social Behavior ; Facial Recognition/physiology* ; Social Perception ; Personality/physiology*

Objective:To investigate the mechanisms of baicalin in treating septic acute liver injury through a combination of network pharmacology and animal experiments.Methods:Thirty male C57BL/6 mice (6 weeks old) were divided into five groups ( n=6): control group (normal saline), model group [lipopolysaccharide (LPS) 10 mg/kg, intraperitoneal injection], low-dose baicalin group (10 mg/kg), high-dose baicalin group (20 mg/kg), and baicalin-only group (20 mg/kg, without LPS). Baicalin was administered orally for 14 consecutive days prior to modeling. Mice were sacrificed 24 h after LPS injection. Alanine transaminase, aspartate transaminase liver tissue histopathology were measured; neutrophil infiltration was visualized using immunofluorescence; mRNA expression levels of interleukin (IL)-1β, IL-17, IL-6, and tumor necrosis factor (TNF)-α were detected by RT-qPCR; and the expression of Toll-like receptor 4 (TLR4) and phosphorylated nuclear factor (NF)-κB proteins were analyzed by Western blotting. Results:In the LPS model group, the ALT, AST, and histopathological injury score were (148.60±22.02) U/L, (81.58±11.59) U/L, and 8.50(7.75, 9.25), respectively. These indicators were significantly reduced in the high-dose baicalin group with (77.90±16.79) U/L, (49.92±14.89) U/L, and 1.00(1.00, 2.25) (all P<0.05). Compared with the LPS group, neutrophil infiltration in the liver of high-dose baicalin group was also significantly reduced [1.18%(0.98%, 1.22%) vs. 6.13%(5.41%, 8.69%), P<0.05]. RT-qPCR results showed that the relative mRNA expression levels of inflammatory cytokines IL-1β [(1.03±0.06) vs. (2.60±0.34)], IL-17 [(1.21±0.12) vs. (2.94 ± 0.39)], IL-6 [(1.37±0.26) vs. (2.73±0.18)], and TNF-α [(1.18±0.10) vs. (3.30±0.92)] were significantly decreased in the high-dose baicalin group compared with the LPS group (all P<0.05). Western blot analysis revealed that the relative protein expression levels of TLR4 [(1.25±0.13) vs. (1.73±0.06)] and phosphorylated NF-κB [(1.25±0.25) vs. (1.79±0.12)] were also significantly lower in the high-dose baicalin group (both P<0.05). Conclusion:Baicalin reduces liver injury in septic mice by downregula-ting the expression of pro-inflammatory cytokines IL-1β, IL-6, TNF-α, and IL-17, potentially through the inhibition of the TLR4/NF-κB signaling pathway.

Objective:To establish the model of acute kidney injury (AKI), search for more sensitive and reliable biomarkers.Methods:In April 2018, 100 male Wister rats aged 6 to 8 weeks were selected and randomly divided into experimental group (n=90) and control group (n=10). The experimental group was given Diachalefin (140 mg/kg body weight) by intragastric administration, while the control group was given saline intragastric administration. Ten rats in the experimental group were killed 0.5 h, 2 h, 6 h, 24 h, 3 d, 7 d, 14 d, 21 d and 28 d after intragastric administration, respectively. Serum creatinine (Cr), urea nitrogen (BUN) and uric acid (UA) were detected by automatic biochemical analyzer with 5 ml of blood from inferior vena cava puncture. Serum neutrophil gelatinase-associated lipid carrier protein (NGAL), kidney damage molecule-1 (KIM-1) and transforming growth factor-β1 (TGF-β1) levels were determined by enzyme-linked immunosorbent assay (ELISA). The data between groups were compared using two independent sample t tests.Results:The renal tissue structure of rats in the control group was not significantly abnormal, while the renal tissue cell damage of rats in the experimental group was obvious, which gradually increased with the extension of time in the early stage, and gradually recovered in the later stage. UA in experimental group reached its peak at 24 h after exposure and was still higher than that in control group at 14 d ( P<0.05), Cr reached its peak at 7 d, and then gradually decreased, and there was no statistical significance between experimental group and control group at 28 d ( P>0.05). BUN increased at 6 h after exposure and reached the highest value at 7~14 d ( P<0.05). Blood NGAL increased at 0.5 h after exposure, reached its peak at 24 h, continued to increase at 3, 7 and 14 days ( P<0.05), and began to decrease at 21 days. KIM-1 began to increase at 0.5 h, continued to peak at 24 h, 3 and 7 d after exposure, and began to decrease at 14 d, but it was still higher than that in control group ( P<0.05). There was no significant difference in TGF-β1 at each time point ( P>0.05). Western blot assay results: Compared with control group, there was no significant difference in the expression level of TGF-β1 in kidney tissue of experimental group ( P>0.05). NGAL increased gradually from 2 h and was higher at 7 and 14 d, with statistical significance ( P<0.05). KIM-1 increased at 2 h, decreased at 6 and 24 h, and increased again at 3 and 7 d. Conclusion:NGAL and KIM-1 can be used as early diagnostic biomarkers for diquat-induced acute kidney injury.

Objective:To analyze the current situation of medical consumables management policy in China,and to provide a reference for the refined management of medical consumables.Methods:Through the policy triangle model and policy tool theory,it comprehensively analyzes the reimbursement medical consumables catalogue and payment management policy of medical insurance in China,covering the policy background,content,process,and participant dimensions.Results:The use frequency of medical consumables policy tools is not balanced,the payment management rules need to be refined,and the participation of multi-stakeholders such as patients is lacking.Conclusion:It is necessary to further strengthen the foundational management of reimbursement medical consumables catalogue,improve the access mechanism of medical consumables for medical insurance,and explore the formulation of categorized payment standards and innovative payment mechanisms.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To analyze the current situation of medical consumables management policy in China,and to provide a reference for the refined management of medical consumables.Methods:Through the policy triangle model and policy tool theory,it comprehensively analyzes the reimbursement medical consumables catalogue and payment management policy of medical insurance in China,covering the policy background,content,process,and participant dimensions.Results:The use frequency of medical consumables policy tools is not balanced,the payment management rules need to be refined,and the participation of multi-stakeholders such as patients is lacking.Conclusion:It is necessary to further strengthen the foundational management of reimbursement medical consumables catalogue,improve the access mechanism of medical consumables for medical insurance,and explore the formulation of categorized payment standards and innovative payment mechanisms.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.