Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

Objective : To investigate the effect of the miR-155-5p/silent information regulator 1(sirt1) signaling pathway on the immune function ofCandida albicansinduced Kawasaki disease model mice. Methods : C56BL/6 mice were separated into control group, Kawasaki disease group, antagonist control group, miR-155-5p antagonist group, miR-155-5p antagonist+si-NC group, and miR-155-5p antagonist+si-sirt1 group, with 12 mice in each group. Except for the control group, mice in all other groups were used to construct a Kawasaki disease model by intraperitoneal injection of water-solubleCandida albicans. After successful modeling, administration was performed once a day for 7 days. QRT-PCR was applied to detect the expression of miR-155-5p in coronary arteries. Western blot was applied to detect sirt1 protein in coronary arteries. HE staining was applied to detect pathological changes in coronary arteries. Mouse thymus index and spleen index were detected. Flow cytometry was applied to detect helper T cells 17(Th17)/regulatory T cells(Treg) in peripheral blood. ELISA was applied to detect the levels of interleukin(IL)-17 and IL-10 in mouse serum. The targeting relationship between sirt1 and miR-155-5p was validated. Results: Compared with the control group, there was a large amount of inflammatory cell infiltration in the coronary arteries of mice in the Kawasaki disease group. The miR-155-5p expression, Th17 ratio, Th17/Treg ratio, and IL-17 level increased. The sirt1 protein expression, thymus index, spleen index, Treg ratio, and IL-10 level decreased(P<0.05). Compared with the Kawasaki disease group, the inflammatory cell infiltration in the coronary arteries of mice in the miR-155-5p antagonist group was alleviated. The miR-155-5p expression, Th17 ratio, Th17/Treg ratio, and IL-17 level decreased. The sirt1 protein expression, thymus index, spleen index, Treg ratio, and IL-10 level increased(P<0.05). Si-sirt1 weakened the promoting effect of miR-155-5p inhibition on Th17/Treg balance and the inhibitory effect on vascular inflammation in Kawasaki disease mice, miR-155-5p targeted and regulated sirt1. Conclusion The mechanism by which inhibiting miR-155-5p promotes Th17/Treg balance and inhibits vascular inflammation in Kawasaki disease mice may be related to the upregulation of sirt1 expression.

OBJECTIVE: To evaluate the clinical efficacy and safety of Yangxue Qingnao Pills (YXQNP) combined with amlodipine in treating patients with grade 1 hypertension. METHODS: This is a multicenter, randomized, double-blind, and placebo-controlled study. Adult patients with grade 1 hypertension of blood deficiency and Gan (Liver)-yang hyperactivity syndrome were randomly divided into the treatment or the control groups at a 1:1 ratio. The treatment group received YXQNP and amlodipine besylate, while the control group received YXQNP's placebo and amlodipine besylate. The treatment duration lasted for 180 days. Outcomes assessed included changes in blood pressure, Chinese medicine (CM) syndrome scores, symptoms and target organ functions before and after treatment in both groups. Additionally, adverse events, such as nausea, vomiting, rash, itching, and diarrhea, were recorded in both groups. RESULTS: A total of 662 subjects were enrolled, of whom 608 (91.8%) completed the trial (306 in the treatment and 302 in the control groups). After 180 days of treatment, the standard deviations and coefficients of variation of systolic and diastolic blood pressure levels were lower in the treatment group compared with the control group. The improvement rates of dizziness, headache, insomnia, and waist soreness were significantly higher in the treatment group compared with the control group (P<0.05). After 30 days of treatment, the overall therapeutic effects on CM clinical syndromes were significantly increased in the treatment group as compared with the control group (P<0.05). After 180 days of treatment, brachial-ankle pulse wave velocity, ankle brachial index and albumin-to-creatinine ratio were improved in both groups, with no statistically significant differences (P>0.05). No serious treatment-related adverse events occurred during the study period. CONCLUSIONS Combination therapy of YXQNP with amlodipine significantly improved symptoms such as dizziness and headache, reduced blood pressure variability, and showed a trend toward lowering urinary microalbumin in hypertensive patients. These findings suggest that this regimen has good clinical efficacy and safety. (Registration No. ChiCTR1900022470).
Humans ; Amlodipine/adverse effects* ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Hypertension/complications* ; Middle Aged ; Treatment Outcome ; Drug Therapy, Combination ; Adult ; Blood Pressure/drug effects* ; Double-Blind Method ; Aged ; Antihypertensive Agents/adverse effects*

Mitochondrial dysfunction in chondrocytes is a key pathogenic factor in osteoarthritis (OA), but directly modulating mitochondria in vivo remains a significant challenge. This study is the first to verify a correlation between mitochondrial dysfunction and the downregulation of the FOXO3 gene in the cartilage of OA patients, highlighting the potential for regulating mitophagy via FOXO3 gene modulation to alleviate OA. Consequently, we developed a chondrocyte-targeting CRISPR/Cas9-based FOXO3 gene-editing tool (FoxO3) and integrated it within a nanoengineered 'truck' (NETT, FoxO3-NETT). This was further encapsulated in injectable hydrogel microspheres (FoxO3-NETT@SMs) to harness the antioxidant properties of sodium alginate and the enhanced lubrication of hybrid exosomes. Collectively, these FoxO3-NETT@SMs successfully activate mitophagy and rebalance mitochondrial function in OA chondrocytes through the Foxo3 gene-modulated PINK1/Parkin pathway. As a result, FoxO3-NETT@SMs stimulate chondrocytes proliferation, migration, and ECM production in vitro, and effectively alleviate OA progression in vivo, demonstrating significant potential for clinical applications.

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

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 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 explore the effects of levosimendan on cardiac function， hemodynamics and prognosis of patients with septic shock complicated with myocardial depression， and evaluate the safety of levosimendan. METHODS Patients with septic shock complicated with myocardial depression who were admitted to the Department of Critical Care Medicine of Chongqing University Three Gorges Hospital from April 2021 to August 2023， underwent adequate fluid resuscitation， had a mean arterial pressure （MAP） ≥65 mmHg， and received pulse indicator continuous cardiac output （PiCCO） monitoring were enrolled. The patients were randomly divided into dobutamine group and levosimendan group according to a random number table， with 20 patients in each group. Both groups received intravenous infusion of Norepinephrine bitartrate injection at a dose of 0.1-2.0 μg/（kg·min）. On this basis， the dobutamine group additionally received intravenous infusion of Dobutamine hydrochloride injection at a dose of 5- 10 μg/（kg·min） for 3 to 7 days， while the levosimendan group additionally received intravenous infusion of Levosimendan injection at a dose of 0.1-0.2 μg/（kg·min） for 24 hours. Heart rate （HR） and hemodynamic parameters [systolic blood pressure， diastolic blood pressure， MAP， central venous pressure （CVP）]， PiCCO monitoring parameters [cardiac function index （CFI）， cardiac index （CI）， stroke volume index （SVI）， extravascular lung water index， global end-diastolic volume index， pulmonary vascular permeability index （PVPI）， global ejection fraction （GEF）， systemic vascular resistance index， left ventricular contractility index]， and prognosis indicators [death within 3 days after administration， mechanical ventilation time，intensive care unit （ICU） stay time， 28-day mortality rate] were compared between the two groups before treatment and at 24 and 72 hours after treatment. Adverse reactions were E-mail：recorded for both groups. RESULTS Compared with before treatment in the same group， CFI， CI and GEF at 24 hours after treatment， CI and GEF at 72 hours after treatment in the dobutamine group， as well as SVI at 24 hours after treatment and SVI and GEF at 72 hours after treatment in the levosimendan group were significantly increased； PVPI at 72 hours after treatment in the dobutamine group was significantly decreased （P＜0.05）. Compared with the dobutamine group during the same period， patients in the levosimendan group had significantly lower HR and significantly higher CVP at 24 hours after treatment （P＜0.05）. Within 3 days after administration， there were no deaths in either group； there were no statistically significant differences in mechanical ventilation time， ICU stay time， 28-day mortality rate， or the incidence of adverse reactions between the two groups （P＞0.05）. CONCLUSIONS For patients with septic shock complicated with myocardial depression who have undergone adequate fluid resuscitation and have a MAP of ≥65 mmHg， levosimendan is comparable to dobutamine in improving cardiac function and hemodynamic parameters， without affecting patients’ prognosis or increasing the risk of adverse reactions such as hypotension.

Objective:To provide reference for clinical diagnosis and treatment of hepatic cystic echinococcosis by analyzing the clinical characteristics of these patients.Methods:Clinical data of 15 patients with hepatic cystic echinococcosis hospitalized in the Second Hospital of Lanzhou University from April 22, 2019 to March 5, 2022 were collected. The general data, clinical manifestations, abdominal imaging results, treatment and outcome of the included patients were retrospectively analyzed.Results:The age of 15 patients with hepatic cystic echinococcosis ranged from 39 to 78 years, with a median age of 51 years. Among the 15 patients, 12 were farmers, two were freelance workers, and one was unknown. Eleven patients presented with abdominal pain and fatigue, one patient presented with obstructive jaundice, and three patients whom were found by physical examination did not complain of obvious discomfort. Echinococcoid cysts were located at the right lobe of the liver in eight patients, at the left lobe of the liver in five patients, and with multiple lesions in the left and right lobes of the liver in two patients. Two patients received conservative therapy. All the 13 patients who received surgical treatment recovered without complications, such as biliary fistula and subphrenic infection. The clinical symptoms including abdominal pain and fatigue were relieved significantly after surgery. The hospital stay were four to 23 days. All patients were administrated with albendazole (400 mg once daily).Conclusions:Hepatic cystic echinococcosis is more common in the right lobe of the liver, with atypical clinical symptoms. Preoperative imaging diagnosis and postoperative pathological examination are necessary for diagnosis. Surgery is the most effective treatment at present, and albendazole should be taken regularly in all patients.