Objective To assess differences in brain activation between active and passive movement of the right hand using blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). Methods Nine healthy adult right handed volunteers were studied. fMRI was performed with active and passive finger-to-finger movement. Results Right hand active and passive movement produced significant activation in the contralateral sensorimotor cortex ( SMC ), the contralateral premotor cortex ( PMC ), bilaterally in the supplementary motor area (SMA) and in the ipsilateral cerebellum. The activated brain areas were centered on the contralateral SMC and PMC and located more forward during active movement than during passive movement. The contralateral SMC was the most strongly and the most frequently activated brain area. The contralateral posterior parietal cortex (PPC) was less relevant to the hand movements. Unlike active movement, passivemovement activated more areas in the posterior central gyrus than in the anterior central gyrus. Conclusions Both active and passive movement significantly activate the brain areas which are responsible for hand movement, but there are some differences in the locations of the cortex areas activated and in the incidence activation except in the contralateral SMC.

Objective:To establish and validate a nomogram model for early prediction of the risk of acute pancreatitis (AP) progressing to severe acute pancreatitis (SAP).Methods:CT signs and clinical laboratory parameters of 361 AP patients admitted to our Hospital from January 2016 to July 2022 were retrospectively collected. There were 221 males (61.2%) and 140 females (38.8%). According to the Atlantic score, all patients were divided into the SAP group (64 cases) and the non-SAP (NSAP) group (297 cases). Univariate analysis was used to screen out variables with statistically significant differences. Multivariate Logistic regression analysis was used to screen out the independent risk factors of SAP, and finally a nomogram prediction model was established. Receiver operating characteristic (ROC) curve, calibration curve and decision curve (DCA) were used to evaluate the predictive efficacy, accuracy and clinical practicability of the model, and Bootstrap method was used to verify the model internally.Results:Univariate analysis and multivariate Logistic regression analysis showed that pleural effusion ( OR=7.353, 95% CI: 3.344-16.170), posterior pararenal space (PPS) involvement ( OR=3.149, 95% CI: 1.314-7.527), serum creatinine concentration (Cr) ( OR=1.027, 95% CI: 1.017-1.038) and serum calcium concentration (Ca 2+) ( OR=0.038, 95% CI: 0.009-0.166) were independent risk factors for SAP ( P<0.05). A Nomogram model was established based on these four factors. The area under the ROC curve (AUC) of this model was 0.905 (95% CI: 0.869-0.933), indicating high predictive efficiency. Internal verification showed that the model had good accuracy in predicting SAP, and C-index was 0.90. DCA analysis showed that the model had high clinical practicability. Conclusions:The Nomogram model combining pleural effusion, PPS involvement, Cr and Ca 2+ had a good effect on early prediction of SAP, which could provide a new reference tool for clinical diagnosis and treatment.

Objective:To explore the value of the Nomogram model established by CT radiomics combined with clinical indicators for prediction of the severity of early acute pancreatitis (AP).Methods:From January 2016 to March 2023, the AP patients in the Second Affiliated Hospital of Soochow University were retrospectively collected. According to the revised Atlanta classification and definition of acute pancreatitis in 2012, all patients were divided into the severe group and the non-severe group. All patients were first diagnosed, and abdominal CT plain scan and enhanced scan were completed within 1 week. Patients were randomly (random number) divided into training and validation groups at a ratio of 7:3. The pancreatic parenchyma was delineated as the region of interest on each phase CT images, and the radiomics features were extracted by python software. LASSO regression and 10-fold cross-validation were used to reduce the dimension and select the optimal features to establish the radiomics signature. Multivariate Logistic regression was used to select the independent predictors of severe acute pancreatitis (SAP), and a clinical model was established. A Nomogram model was established by combining CT radiomics signature and clinical independent predictors. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive efficacy of each model.Results:Total of 205 AP patients were included (59 cases in severe group, 146 cases in non-severe group). 3, 5, 5 and 5 optimal radiomics features were selected from the plain CT scan, arterial phase, venous phase and delayed phase images of all patients, and the radiomics models were established. Among them, the arterial phase radiomics model had relatively better performance in predicting SAP, with an area under curve (AUC) of 0.937 in the training group and 0.913 in the validation group. Multivariate Logistic regression showed that C-reactive protein (CRP) and lactate dehydrogenase (LDH) were independent predictors of SAP, and they were used to establish a clinical model. The AUC in the training and validation groups were 0.879 and 0.889, respectively. The Nomogram model based on arterial phase CT radiomics signature, CRP and LDH was established, and the AUC was 0.956 and 0.947 in the training group and validation group, respectively. DCA showed that the net benefit of Nomogram model was higher than that of clinical model or radiomics model alone.Conclusions:The Nomogram model established by CT radiomics combined with clinical indicators has high application value for early prediction of the severity of AP, which is conducive to the formulation of clinical treatment plans and prognosis evaluation.

The in-depth study of the intervention of endometriosis （EMS） with Chinese medicines has revealed many pathways that can be regulated by Chinese medicines in the treatment of EMS. Chinese medicinal compound prescriptions， single Chinese herbal medicines， or their active ingredients treat EMS via the signaling pathways discussed in this paper. They can directly or indirectly regulate the expression of key molecules in the corresponding signaling pathways to inhibit the proliferation of endometriotic cells and the inflammatory changes of endometrial tissue， promote the apoptosis of endometriotic cells， change the pain threshold， and reduce endometriotic cell invasion， so as to achieve the therapeutic effects of inhibiting EMS progression， improving endometrial receptivity， and reducing ovarian injury. The following details are based on relevant studies conducted both at home and abroad. Curcumol can inhibit angiogenesis and promote cell apoptosis by blocking the Janus kinase 2/signal transducer and activator of transcription 3 signaling pathway. Icariin can reduce inflammation and promote cell apoptosis by blocking the nuclear factor-kappa B signaling pathway. Puerarin can suppress cell proliferation and promote cell apoptosis by inhibiting the mitogen-activated protein kinase （MAPK） signaling pathway and blocking the estrogen signaling pathway. Naringenin has the ability to activate the MAPK signaling pathway to promote cell apoptosis. Imperatorin can inhibit cell proliferation and promote cell apoptosis by inhibiting the phosphatidylinositol 3-kinase/protein kinase B signaling pathway. Resveratrol can inhibit cell proliferation and alleviate fibrosis and adhesion by blocking the transforming growth factor-beta signaling pathway. Paeonol can inhibit angiogenesis by blocking the hypoxia-inducible factor 1 signaling pathway. The above summary can serve as a reference for the future clinical treatment or experimental research of EMS with Chinese medicines.