OBJECTIVE To optimize the management model of drugs used in investigator-initiated trial （IIT）. METHODS With benchmarking analysis， based on the practical work experience of a tertiary specialized hospital in the field of IIT drug management in Shanghai， a thorough review was conducted， involving relevant laws， regulations， and academic literature to establish benchmark criteria and the evaluation standards. Starting from the initiation of IIT projects， a detailed comparative analysis of key processes was carried out， such as the receipt， storage， distribution， use and recycling of drugs for trial. The deficiencies in the current management of IIT drugs were reviewed in detail and a series of optimization suggestions were put forward. RESULTS It was found that the authorized records of drug management were missing， the training before project implementation was insufficient， and the records of receipt and acceptance of IIT drugs were incomplete. In light of these existing problems， improvement measures were put forward， including strengthening the training of drug administrators and stipulating that only drug administrators with pharmacist qualifications be eligible to inspect and accept drugs， etc. The related systems were improved， and 17 key points of quality control for the management of IIT drugs were developed. CONCLUSIONS A preliminary IIT drug management system for medical institutions has been established， which helps to improve the institutional X2023076） framework of medical institutions in this field.

The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

As the number of patients with compromised immune function increases and fungal resistance develops, so does the risk of contracting deadly fungi in humans. Both fungi and humans are eukaryotes, so identifying unique targets for antifungal drug development is difficult. In addition, the existing antifungal drugs are limited by toxicity, drug interaction and drug resistance in practical application, which leads to the increasing incidence and fatal rate of fungal infections. Therefore, it is urgent to develop new antifungal drugs. The semi-synthetic technology using microbial fermentation products from natural sources as lead compounds has become the most used method in structural modification of antifungal drugs due to its advantages of few reaction steps and easy operation. This paper will introduce the current status of natural antifungal drugs in clinical use, as well as the latest progress in the research and development of new semi-synthetic antifungal drugs, and summarize their mechanism of action, structural modifications, advantages and disadvantages, so as to provide reference for the subsequent development of new antifungal drugs.

Fibrosis, the pathological scarring of vital organs, is a severe and often irreversible condition that leads to progressive organ dysfunction. It is particularly pronounced in organs like the liver, kidneys, lungs, and heart. Despite its clinical significance, the full understanding of its etiology and complex pathogenesis remains incomplete, posing substantial challenges to diagnosing, treating, and preventing the progression of fibrosis. Among the various molecular players involved, platelet-derived growth factor-C (PDGF-C) has emerged as a crucial factor in fibrotic diseases, contributing to the pathological transformation of tissues in several key organs. PDGF-C is a member of the PDGFs family of growth factors and is synthesized and secreted by various cell types, including fibroblasts, smooth muscle cells, and endothelial cells. It acts through both autocrine and paracrine mechanisms, exerting its biological effects by binding to and activating the PDGF receptors (PDGFRs), specifically PDGFRα and PDGFRβ. This binding triggers multiple intracellular signaling pathways, such as JAK/STAT, PI3K/AKT and Ras-MAPK pathways. which are integral to the regulation of cell proliferation, survival, migration, and fibrosis. Notably, PDGF-C has been shown to promote the proliferation and migration of fibroblasts, key effector cells in the fibrotic process, thus accelerating the accumulation of extracellular matrix components and the formation of fibrotic tissue. Numerous studies have documented an upregulation of PDGF-C expression in various fibrotic diseases, suggesting its significant role in the initiation and progression of fibrosis. For instance, in liver fibrosis, PDGF-C stimulates hepatic stellate cell activation, contributing to the excessive deposition of collagen and other extracellular matrix proteins. Similarly, in pulmonary fibrosis, PDGF-C enhances the migration of fibroblasts into the damaged areas of lungs, thereby worsening the pathological process. Such findings highlight the pivotal role of PDGF-C in fibrotic diseases and underscore its potential as a therapeutic target for these conditions. Given its central role in the pathogenesis of fibrosis, PDGF-C has become an attractive target for therapeutic intervention. Several studies have focused on developing inhibitors that block the PDGF-C/PDGFR signaling pathway. These inhibitors aim to reduce fibroblast activation, prevent the excessive accumulation of extracellular matrix components, and halt the progression of fibrosis. Preclinical studies have demonstrated the efficacy of such inhibitors in animal models of liver, kidney, and lung fibrosis, with promising results in reducing fibrotic lesions and improving organ function. Furthermore, several clinical inhibitors, such as Olaratumab and Seralutinib, are ongoing to assess the safety and efficacy of these inhibitors in human patients, offering hope for novel therapeutic options in the treatment of fibrotic diseases. In conclusion, PDGF-C plays a critical role in the development and progression of fibrosis in vital organs. Its ability to regulate fibroblast activity and influence key signaling pathways makes it a promising target for therapeutic strategies aiming at combating fibrosis. Ongoing research into the regulation of PDGF-C expression and the development of PDGF-C/PDGFR inhibitors holds the potential to offer new insights and approaches for the diagnosis, treatment, and prevention of fibrotic diseases. Ultimately, these efforts may lead to the development of more effective and targeted therapies that can mitigate the impact of fibrosis and improve patient outcomes.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

Objective To explore the occurrence and development trajectories of symptoms at different time points in patients with acute pancreatitis (AP), and to analyze the influencing factors and preventive measures of development trajectories of AP symptom clusters. Methods A convenient sampling method was used to select AP who were admitted from January 2023 to December 2023 were selected and included in the study. The symptoms at different time points were recorded. The severities of symptom clusters in AP patients were explored, and the development trajectories of main symptom clusters were analyzed. Univariate and multivariate logistic regression analyses were used to analyze the influencing factors of development trajectories of symptom clusters in AP patients. Results The incidence rates of abdominal pain, dry mouth, abdominal distension and lack of energy were higher in AP patients during hospitalization. The incidence rates of lack of energy, anxiety, abdominal pain and sleep disturbance were higher on the 1^st month after discharge. The incidence rates of abdominal distension, abdominal pain, sleep disturbance and anxiety were higher on the 3^rd month after discharge. The incidence rates of anxiety, abdominal pain and irritability were higher on the 6^th month after discharge. The fatigue symptom cluster, psychological symptom cluster and gastrointestinal symptom cluster were extracted during hospitalization and on the 1^st month and the 3^rd month after discharge, and the psychological symptom cluster and gastrointestinal symptom cluster were extracted on the 6^th month. The severity scores of symptom clusters at each time point were statistically different (P<0.05). The development of gastrointestinal symptom cluster in AP patients was mainly low decline. The development of psychological symptom cluster was mainly high decline. Drinking history and diabetes mellitus were the influencing factors of development trajectory of gastrointestinal symptom cluster in AP patients (P<0.05). High disease severity, drinking history and biliary tract disease were the influencing factors of development trajectory of psychological symptom cluster in AP patients (P<0.05). Conclusion The symptom clusters of AP patients changes over time, with digestive, fatigue, and psychological symptoms being the main groups in the early stage, and psychological and digestive symptoms persisting in the later stage. Early identification and intervention are crucial for improving the prognosis of AP patients.

OBJECTIVE: To explore the effect mechanism of electroacupuncture (EA) for ameliorating diabetic peripheral neuropathy (DPN) based on the analysis of gut microbiota and metabolomics. METHODS: Thirty SPF-grade male SD rats were randomly divided into a normal group, a model group, and an EA group, with 10 rats in each one. Except in the normal group, the intraperitoneally injection with streptozotocin was used to induce diabetes mellitus model in the rest groups. In the EA group, acupuncture was delivered at bilateral "Zusanli" (ST36), "Sanyinjiao" (SP6), "Pishu" (BL20) and "Shenshu" (BL23), and electric stimulation was attached to "Zusanli" (ST36)-"Sanyinjiao" (SP6) and "Pishu" (BL20)-"Shenshu" (BL23), on the same side, with continuous wave and a frequency of 2 Hz, for 10 min in each intervention. The intervention measure of each group was delivered once every 2 days, 3 times a week, for 8 consecutive weeks. Body weight, random blood glucose (RBG), thermal withdrawal latency (TWL), and mechanical withdrawal threshold (MWT) before intervention, and in 4 and 8 weeks of intervention, separately, as well as sensory nerve conduction velocity (SCV) and motor nerve conduction velocity (MCV) of the sciatic nerve after intervention were measured. Metagenomic sequencing (MS) was used to analyze gut microbiota and screen for differential species. Liquid chromatography-mass spectrometry (LC-MS) was employed to detect the differential metabolites in plasma, and the metabolic pathway enrichment analysis was performed on the differential metabolites. Spearman correlation analysis was adopted to assess the relationship between gut microbiota and metabolomics. RESULTS: After 4 and 8 weeks of intervention, when compared with the model group, the EA group showed the increase in body weight, TWL, MWT (P<0.01), and the decrease in RBG (P<0.01). Compared with the normal group, SCV and MCV, as well as Chao1 index were dropped in the model group (P<0.01), and those were elevated in the EA group when compared with those in the model group (P<0.01). The dominant bacterial phyla of each group were Firmicutes (F) and Bacteroidota (B), the ratio of them (F/B) in the model group was lower than that of the normal group (P<0.05), and F/B in the EA group was higher when compared with that in the model group (P<0.05). In comparison with the normal group, the relative abundance increased in Prevotella, Segatella, Prevotella-hominis and Segatella-copri (P<0.05); and it decreased in Ligilactobacillus, Eubacterium, Pseudoflavonifractor, Ligilactobacillus-murinus (P<0.05) in the model group. Compared with the model group, the relevant abundance of the above mentioned gut bacteria was all ameliorated in the EA group (P<0.05, P<0.01). Among the three groups, 120 differential metabolites were identified and enriched in 28 key metabolic pathways, such as glycerophospholipid and linoleic acid, of which, glycerophospholipid was the most significantly affected pathway in EA intervention. Spearman correlation analysis showed that 6 phosphatidylcholine metabolites were significantly positively correlated with Pseudoflavonifractor and were negatively with Prevotella, Segatella, Prevotella-hominis, Segatella-copri; 5 phosphatidylethanolamine metabolites were significantly negatively correlated with Pseudoflavonifractor and positively correlated with Prevotella, Segatella, Prevotella-hominis, Segatella-copri. CONCLUSION EA may regulate metabolic pathways such as glycerophospholipid, modulate specific gut microbiota such as Pseudoflavonifractor, Prevotella, and Segatella, and the co-expressed differential metabolites like phosphatidylcholine and phosphatidylethanolamine, thereby reducing blood glucose and protecting nerve function, so as to relieve the symptoms of DPN of rats.
Animals ; Electroacupuncture ; Male ; Gastrointestinal Microbiome ; Diabetic Neuropathies/microbiology* ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Humans ; Acupuncture Points