Obesity is a significant risk factor for cancer and is associated with breast cancer metastasis. Nevertheless, the mechanism by which alterations in systemic metabolism affect tumor microenvironment (TME) and consequently influence tumor metastasis remains inadequately understood. Herein, we found that perturbations in circulating metabolites induced by obesity promote metastasis-like phenotypes in breast cancer. Oleoylcarnitine (OLCarn) concentrations were elevated in the serum of obese mice and humans. Administration of exogenous OLCarn induces metastasis-like characteristics in breast cancer cells. Mechanistically, OLCarn directly interacts with the Arg176 site of adenylate cyclase 10 (ADCY10), leading to the activation of ADCY10 and enhancement of cAMP production. Mutations at Arg176 prevent OLCarn from binding to ADCY10, disrupting the ADCY10-mediated activation of cyclic adenosine monophosphate (cAMP) signaling pathway. This activation promotes transcription factor 4 (TCF4)-dependent kinesin family member C1 (KIFC1) transcription, thereby driving breast cancer metastasis. Conversely, the neutralization of both ADCY10 and KIFC1 through knockdown or pharmacological inhibition abrogates the oncogenic effects mediated by OLCarn. Hence, obesity-induced systemic environmental changes lead to the aberrant accumulation of OLCarn within the TME, making it a potential therapeutic target and biomarker for breast cancer.

With the rapid development of artificial intelligence(AI),how to digitize the teaching of Medical Immunology is a new challenge posed by the times and education.This study is based on the advanced teaching model of Medical Immunology,which includes lectures-PAD class-flipped classrooms-expert lecture.By introducing knowledge mapping and AI teaching assistant into the entire learning process,the students not only deepen their understanding of the knowledge system of Medical Immunology,but also ex-ercise their ability to apply immunological knowledge to solve practical clinical problems,enhance their self-learning ability,expres-sion ability,communication ability,on-site performance ability,and cultivate a spirit of unity,cooperation,and exploration.The practice of empowering Medical Immunology teaching with digital intelligence achieves the integration of theory and application,the linkage between in class and out of class teaching,the connection between commonalities and individualities,and the union of abili-ties and qualities in Medical Immunology teaching.It also provides practical basis for exploring the implementation path of digital intel-ligence empowerment in Medical Immunology teaching.

Theory and technology of Medical Immunology are widely used in scientific research.Our teaching and research group uses experimental teaching of Medical Immunology as a platform to carry out practice of scientific research project-based experi-mental system among"5+3"integration students.By completing a mini-project research including experimental design-experimental operation-research article writing,students cultivated scientific research thinking and exercised scientific research practice ability,and generally reported that the course is very difficult,but after completing it,it is very rewarding.

Ovarian cancer poses a significant threat to women's health, necessitating effective therapeutic strategies. Emd-D, an emodin derivative, demonstrates enhanced pharmaceutical properties and bioavailability. In this study, Cell Counting Kit 8 (CCK8) assays and Ki-67 staining revealed dose-dependent inhibition of cell proliferation by Emd-D. Migration and invasion experiments confirmed its inhibitory effects on OVHM cells, while flow cytometry analysis demonstrated Emd-D-induced apoptosis. Mechanistic investigations elucidated that Emd-D functions as an inhibitor by directly binding to the glycolysis-related enzyme PFKFB4. This was corroborated by alterations in intracellular lactate and pyruvate levels, as well as glucose transporter 1 (GLUT1) and hexokinase 2 (HK2) expression. PFKFB4 overexpression experiments further supported the dependence of Emd-D on PFKFB4-mediated glycolysis and SRC3/mTORC1 pathway-associated apoptosis. In vivo experiments exhibited reduced xenograft tumor sizes upon Emd-D treatment, accompanied by suppressed glycolysis and increased expression of Bax/Bcl-2 apoptotic proteins within the tumors. In conclusion, our findings demonstrate Emd-D's potential as an anti-ovarian cancer agent through inhibition of the PFKFB4-dependent glycolysis pathway and induction of apoptosis. These results provide a foundation for further exploration of Emd-D as a promising drug candidate for ovarian cancer treatment.
Female ; Humans ; Ovarian Neoplasms/physiopathology* ; Phosphofructokinase-2/genetics* ; Apoptosis/drug effects* ; Glycolysis/drug effects* ; Animals ; Cell Line, Tumor ; Mice ; Cell Proliferation/drug effects* ; Emodin/administration & dosage* ; Mice, Nude ; Mice, Inbred BALB C ; Hexokinase/metabolism* ; Xenograft Model Antitumor Assays

With the rapid development of artificial intelligence(AI),how to digitize the teaching of Medical Immunology is a new challenge posed by the times and education.This study is based on the advanced teaching model of Medical Immunology,which includes lectures-PAD class-flipped classrooms-expert lecture.By introducing knowledge mapping and AI teaching assistant into the entire learning process,the students not only deepen their understanding of the knowledge system of Medical Immunology,but also ex-ercise their ability to apply immunological knowledge to solve practical clinical problems,enhance their self-learning ability,expres-sion ability,communication ability,on-site performance ability,and cultivate a spirit of unity,cooperation,and exploration.The practice of empowering Medical Immunology teaching with digital intelligence achieves the integration of theory and application,the linkage between in class and out of class teaching,the connection between commonalities and individualities,and the union of abili-ties and qualities in Medical Immunology teaching.It also provides practical basis for exploring the implementation path of digital intel-ligence empowerment in Medical Immunology teaching.

Theory and technology of Medical Immunology are widely used in scientific research.Our teaching and research group uses experimental teaching of Medical Immunology as a platform to carry out practice of scientific research project-based experi-mental system among"5+3"integration students.By completing a mini-project research including experimental design-experimental operation-research article writing,students cultivated scientific research thinking and exercised scientific research practice ability,and generally reported that the course is very difficult,but after completing it,it is very rewarding.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.

The goal of Medical Immunology is to enable students to pay attention to integration of immunology theory with clini-cal practice,be familiar with professional English,consciously pay attention to cutting-edge knowledge,and can learn independently and lifelong.However,existing teaching models lack explanation of clinical disease related events,and arrangement of experimental content and projects is seriously disconnected from clinical practice,scientific research on solving clinical problems is clearly insuffi-cient.We established a"B to B"cycle model for immunology teaching by starting from clinical practical problems(Bedside),return to basic research(Bench),and then solve clinical problem(Bedside),which will comprehensively cultivate senior medical profession-als with clinical competence,scientific research thinking ability,innovative spirit,and international perspective.

Objective:To investigate the diagnostic accuracy of serological indicators and evaluate the diagnostic value of a new established combined serological model on identifying the minimal hepatic encephalopathy (MHE) in patients with compensated cirrhosis.Methods:This prospective multicenter study enrolled 263 compensated cirrhotic patients from 23 hospitals in 15 provinces, autonomous regions and municipalities of China between October 2021 and August 2022. Clinical data and laboratory test results were collected, and the model for end-stage liver disease (MELD) score was calculated. Ammonia level was corrected to the upper limit of normal (AMM-ULN) by the baseline blood ammonia measurements/upper limit of the normal reference value. MHE was diagnosed by combined abnormal number connection test-A and abnormal digit symbol test as suggested by Guidelines on the management of hepatic encephalopathy in cirrhosis. The patients were randomly divided (7∶3) into training set ( n=185) and validation set ( n=78) based on caret package of R language. Logistic regression was used to establish a combined model of MHE diagnosis. The diagnostic performance was evaluated by the area under the curve (AUC) of receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve. The internal verification was carried out by the Bootstrap method ( n=200). AUC comparisons were achieved using the Delong test. Results:In the training set, prevalence of MHE was 37.8% (70/185). There were statistically significant differences in AMM-ULN, albumin, platelet, alkaline phosphatase, international normalized ratio, MELD score and education between non-MHE group and MHE group (all P<0.05). Multivariate Logistic regression analysis showed that AMM-ULN [odds ratio ( OR)=1.78, 95% confidence interval ( CI) 1.05-3.14, P=0.038] and MELD score ( OR=1.11, 95% CI 1.04-1.20, P=0.002) were independent risk factors for MHE, and the AUC for predicting MHE were 0.663, 0.625, respectively. Compared with the use of blood AMM-ULN and MELD score alone, the AUC of the combined model of AMM-ULN, MELD score and education exhibited better predictive performance in determining the presence of MHE was 0.755, the specificity and sensitivity was 85.2% and 55.7%, respectively. Hosmer-Lemeshow test and calibration curve showed that the model had good calibration ( P=0.733). The AUC for internal validation of the combined model for diagnosing MHE was 0.752. In the validation set, the AUC of the combined model for diagnosing MHE was 0.794, and Hosmer-Lemeshow test showed good calibration ( P=0.841). Conclusion:Use of the combined model including AMM-ULN, MELD score and education could improve the predictive efficiency of MHE among patients with compensated cirrhosis.

Under background of new medical science,how to train high-quality medical talents with post competence is one of important contents of medical higher education.In order to train professional medical talents in different fields,make them more suitable for characteristics of their majors,and adapt to needs of future career development,"precision teaching"should be carried out in teaching,including precision of course content design,teaching mode and assessment method,so as to achieve goal of cultivating professional medical talents with precision.Therefore,guided by competency of new medical posts,our teaching team try to explore precision teaching in course construction of Medical Immunology.Here we introduce specific method by taking chapter of Antibody as an example.