The incidence of intrahepatic cholangiocarcinoma (ICC) continues to rise, and there are no effective drugs to treat it. The immune microenvironment plays an important role in the development of ICC and is currently a research hotspot. Icaritin (ICA) is an innovative traditional Chinese medicine for the treatment of advanced hepatocellular carcinoma. It is considered to have potential immunoregulatory and anti-tumor effects, which is potentially consistent with the understanding of "Fuzheng" in the treatment of tumor in traditional Chinese medicine. However, whether ICA can be used to treat ICC has not been reported. Therefore, in this study, sgp19/kRas, an in situ ICC mouse model with intact immune system, was selected to evaluate the efficacy of ICA in the treatment of ICC in vivo for the first time, and the effects of ICA on the tumor immune microenvironment of ICC mice were analyzed by flow cytometry. This experiment was approved by the Experimental Animal Ethics Committee of Capital Medical University (approval number: AEEI-2023-138). In this study, sgp19/kRas ICC mouse model was treated with oral gavage of 100 mg·kg^-1 ICA. We found that ICA significantly inhibited tumor growth and tumor cell proliferation in the sgp19/kRas ICC mouse model after 3 weeks of treatment. Flow cytometry analysis indicated that ICA treatment markedly reduced the proportion of M2 macrophages and increased the number of CD3⁺CD4⁺ T cells. In vitro experiments demonstrated that ICA promoted macrophage polarization towards the M1 phenotype while inhibiting polarization towards the M2 phenotype. Furthermore, transcriptomic analysis suggested that ICA enhanced Toll-like receptor 9 (TLR9) expression, influencing macrophage nitric oxide metabolism synthesis pathways and receptor-related activities, thereby regulating macrophage polarization. In summary, this study demonstrates that ICA treatment significantly delays tumor progression in sgp19/kRas ICC mouse models. Mechanistically, ICA may achieve its anti-ICC effects by upregulating TLR9 receptor expression levels, promoting macrophage polarization towards the M1 phenotype, and altering the tumor immune microenvironment.

ObjectiveTo evaluate the effectiveness of Lutongning granules in the treatment of trigeminal neuralgia in animal models and study its mechanism of action， so as to provide laboratory data support for the clinical application of Lutongning granules and precise treatment. MethodMale SD rats were randomly divided into normal group， model group， carbamazepine group （0.06 g·kg^-1·d^-1）， high-dose Lutongning group （2.70 g·kg^-1·d^-1）， and low-dose Lutongning group （1.35 g·kg^-1·d^-1） according to the stratified basic mechanical pain thresholds， with 10 rats in each group. A trigeminal neuralgia model of rats was prepared by injecting 30% talc suspension into the infraorbital foramen area of the rat. The drug groups were administered 10 mL·kg^-1 of drugs by gavage after 2 h of modeling. The normal group and the model group were administered distilled water by gavage under the same conditions once a day for 10 consecutive days. Von Frey brushes were used to determine the mechanical pain threshold of rats. A fully automated blood and body fluid analyzer was employed to detect the blood routine of rats. Hematoxylin and eosin （HE） staining was utilized to detect the pathological changes in the trigeminal ganglion and medulla oblongata tissue. Transmission electron microscopy was used to scan the ultrastructure of the medulla oblongata tissue. Enzyme-linked immunosorbent assay （ELISA） was used to detect the levels of inflammatory factors interleukin （IL）-1， IL-6， IL-8， tumor necrosis factor （TNF）-α， neuropeptide substance P， and β-endorphins （β-EP） in the serum of rats， and Western blot was used to detect the protein expression levels of IL-1β， purinergic receptor P2X7 （P2X7R）， and phosphorylated p38 mitogen-activated protein kinase （p-p38 MAPK）. ResultCompared with that in the normal group， the pain threshold of rats in the model group was significantly lower （P<0.01）. The absolute value of neutrophils （NEUT#） and the percentage of neutrophils （NEUT） were significantly improved， and the percentage of lymphocytes （LYMPH） was significantly reduced （P<0.01）. The serum levels of IL-1， IL-6， IL-8， and TNF-α were significantly increased （P<0.01）. SP content in brain tissue was significantly increased， and β-EP content was significantly decreased （P<0.01）. The relative protein expression of IL-1β， P2X7R， and p-p38 MAPK was significantly increased （P<0.05）. HE staining and transmission electron microscopy results of medulla oblongata tissue revealed neuronal degeneration， mild proliferation of microglial cells， reduction in the number of myelinated nerves， and obvious demyelination. The trigeminal nerve fibers of rats were disarranged， and some nerve fibers showed vacuolization. Axons were swollen， and Schwann cells proliferated. Demyelination was observed. Compared with the model group， each administration group significantly increased the pain threshold of rats （P<0.05， P<0.01）， reduced NEUT# and NEUT， and elevated LYMPH （P<0.05， P<0.01）. The administration group significantly decreased the levels of IL-1， IL-6， IL-8， and TNF-α in serum and SP in brain tissue （P<0.01） and increased the level of β-EP （P<0.01）. They significantly down-regulated the protein expression of IL-1β， P2X7R， and p-p38 MAPK（P<0.05， P<0.01） and significantly ameliorated the pathological changes in medulla oblongata tissue and trigeminal nerves of rats. ConclusionLutongning Granules had significant therapeutic effects on trigeminal neuralgia induced by injection of talc into the infraorbital foramen of model rats， and the mechanism may be related to amelioration of P2X7R-mediated neuroinflammation and inhibition of demyelination of myelinated nerves.

The COVID-19 epidemic has spread to the whole world for three years and has had a serious impact on human life, health and economic activities. China's epidemic prevention and control has gone through the following stages: emergency unconventional stage, emergency normalization stage, and the transitional stage from the emergency normalization to the "Category B infectious disease treated as Category B" normalization, and achieved a major and decisive victory. The designated hospitals for prevention and control of COVID-19 epidemic in Tianjin has successfully completed its tasks in all stages of epidemic prevention and control, and has accumulated valuable experience. This article summarizes the experience of constructing a hospital infection prevention and control system during the "Category B infectious disease treated as Category A" period in designated hospital. The experience is summarized as the "Cluster" hospital infection prevention and control system, namely "three rings" outside, middle and inside, "three districts" of green, orange and red, "three things" before, during and after the event, "two-day pre-purification" and "two-director system", and "one zone" management. In emergency situations, we adopt a simplified version of the cluster hospital infection prevention and control system. In emergency situations, a simplified version of the "Cluster" hospital infection prevention and control system can be adopted. This system has the following characteristics: firstly, the system emphasizes the characteristics of "cluster" and the overall management of key measures to avoid any shortcomings. The second, it emphasizes the transformation of infection control concepts to maximize the safety of medical services through infection control. The third, it emphasizes the optimization of the process. The prevention and control measures should be comprehensive and focused, while also preventing excessive use. The measures emphasize the use of the least resources to achieve the best infection control effect. The fourth, it emphasizes the quality control work of infection control, pays attention to the importance of the process, and advocates the concept of "system slimming, process fattening". Fifthly, it emphasizes that the future development depends on artificial intelligence, in order to improve the quality and efficiency of prevention and control to the greatest extent. Sixth, hospitals need to strengthen continuous training and retraining. We utilize diverse training methods, including artificial intelligence, to ensure that infection control policies and procedures are simple. We have established an evaluation and feedback mechanism to ensure that medical personnel are in an emergency state at all times.

Objective:To identify and improve the weakness in different clinical practice teaching hospital,and enhance the quality of practical teaching.Methods:A total of 291 trainees majoring in clinical medicine in grade 2016 in a medical college in Shenyang who practiced in different clinical teaching hospitals and participated in the objective structured clinical examination(OSCE)were enrolled.The OSCE scores was analyzed with one way ANOVA and Kruskal-Wallis statistic methods to identify the weakness in clinical practice teaching and improve the teaching quality.Results:In the standardized patient consultation and physical examination results,the passing rate and average score of H hospital was the lowest.The average score of trainees in H hospital was statistically significant compared to that in A,B,F,G,K,and L hospitals(P＜0.01).In terms of skill operation scores,H hospital had the lowest pass rate and average score.The average score of interns in H hospital was statistically significant compared to that in A,D,E,F,G,I,J,K hospitals(P＜0.01).In the interpretation of auxiliary examination results,the passing rate and average score of H hospital was the lowest.The average score of interns in A and H hospital was significantly different from that of B and J hospitals(P＜0.01).Conclusions:There are great differences in the practice effect of students in different clinical teaching hospitals.Medical colleges and universities should strengthen the management of clinical teaching hospitals to ensure the homogeneity of clinical practice teaching quality.

Iron is indispensable for the viablility of nearly all living organisms, and it is imperative for cells, tissues, and organisms to acquire this essential metal sufficiently and maintain its metabolic stability for survival. Disruption of iron homeostasis can lead to the development of various diseases. There is a robust connection between iron metabolism and infection, immunity, inflammation, and aging, suggesting that disorders in iron metabolism may contribute to the pathogenesis of arthritis. Numerous studies have focused on the significant role of iron metabolism in the development of arthritis and its potential for targeted drug therapy. Targeting iron metabolism offers a promising approach for individualized treatment of arthritis. Therefore, this review aimed to investigate the mechanisms by which the body maintains iron metabolism and the impacts of iron and iron metabolism disorders on arthritis. Furthermore, this review aimed to identify potential therapeutic targets and active substances related to iron metabolism, which could provide promising research directions in this field.

Aim To predict the mechanism of Fufang Congrong Yizhi Capsules (FCYC) in the treatment of mild cognitive impairment (MCI) by network pharmacology method, and further validate it in combination with cellular experiments. Methods TCMSP, Gene-Cards, OMIM and TTD databases, Chinese Pharmacopoeia and related literature were used to screen the active ingredients of FCYC and the targets of MCI treatment. The TCM-compound-target-disease network and PPI of intersection targets were constructed, and the GO and KEGG analysis were performed by the Ehamb bioinformation platform. GO and KEGG analysis were performed through Yihanbo biological information platform. Cell model of MCI was established by PC-12 injury induced by Aβ

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

Child ; Humans ; Inflammatory Bowel Diseases ; Organic Anion Transporters/genetics*